WO2023038571A1

WO2023038571A1 - Multilevel, multivariate loan request evaluation platform and evaluation method

Info

Publication number: WO2023038571A1
Application number: PCT/SG2021/050546
Authority: WO
Inventors: Tze Ken Jonathan Julian TEOH
Original assignee: Teoh Tze Ken Jonathan Julian
Priority date: 2021-09-10
Filing date: 2021-09-10
Publication date: 2023-03-16

Abstract

A general embodiment of the invention is an evaluation platform 11 or an evaluation method for receiving and evaluating each of a plurality of loan requests for subject property. A data aggregator 11C aggregates subject details SD0-N of the subject property and comparable details CD0-N of comparable properties to calculate a normalized present value and a normalized future value range of each of the comparable properties with the assistance of raw data. The raw data includes property transaction information, geospatial data, macroeconomic data, and census data. The normalized present values and normalized future value ranges are used to calculate a subject present value and a subject future value range for the subject property, which are matched with qualification profiles of investors for delivery of qualification packages to the investors according to a delivery protocol for each qualification match.

Description

MULTILEVEL, MULTIVARIATE LOAN REQUEST

EVALUATION PLATFORM AND EVALUATION METHOD

TECHNICAL CONTRIBUTION

The present disclosure relates to automated evaluation of real property loan requests. More particularly, the present disclosure relates to a loan request evaluation platform and evaluation method for receiving and evaluating each of a plurality of loan requests for subject properties.

BACKGROUND

Financial instruments enabling periodic payments from a financial services provider (hereafter, “the investor”) to a financial services customer (hereafter, “the customer”) can be secured by the equity of a customer’s real property. Such financial instruments are often referred to as reverse mortgage loans, home equity lines of credit, home reversion schemes, home equity release requests, or home co-ownership requests (hereafter, “loans”). With each periodic payment over the loan’s lifetime, the customer’s debt increases and the customer’s equity in the real property decreases.

As with the more commonplace forward mortgage loan, the customer’s real property that is the subject of the loan (hereafter, “the subject property”) is the collateral for the loan. When the customer moves or dies, the proceeds from the subject property’s sale go to the lender to repay the loan’s principal, interest, mortgage insurance, and fees. Any sale proceeds beyond what was borrowed go to the customer (if still living) or the customer’s estate (if the customer has died). In some cases, the heirs of the customer may choose to pay off the loan so they can keep the subject property.

The loan is helpful for “cash-poor, asset-rich” retirees who retain significant equity within their real estate, such real estate often including their domicile. As part of a retirement cashflow strategy, the loan enables a retiree to retain legal title and occupancy of their domicile during retirement. The retiree’s ongoing living expenses are covered by the periodic payments received by the customer from the investor for the duration of the loan. Accounting practices treat the periodic payments to the customer as loan principal requiring eventual repayment with interest. For the duration of the loan, there is no sale or conversion of the subject property. Hence the periodic payments are not considered either ordinary income or capital gains income. Depending on local tax laws, appreciation of the subject property for the duration of the loan may also be exempted from capital gains taxes to the extent the appreciation of the subject property is used to pay off the loan accrued from the periodic payments.

The investor providing the loan takes on significant risk. A retiree could retire in their sixties, enter a loan, and then live into their nineties. The periodic payments thus could be paid over several decades. Over these many years, the value of the subject property could either not appreciate in value as expected or the subject property could even diminish in value due to factors such as normal wear and tear aging of the subject property itself, political unrest, natural disasters, poor maintenance, or upcoming expiration of a leasehold for the subject property.

The standard approach of investors to evaluate a loan request (hereafter, “the loan request”) is to perform an appraisal on the subject property to determine an estimated fair market present value (hereafter, “the subject present value”). Future risk for the investor is mitigated by placing a cap upon the sum of the periodic payments from the investor to the customer. Appraisals are performed by qualified appraisal professionals, who locate several comparable properties that have been recently sold on the open market to determine a fair market value of each of the comparable properties (hereafter, “the comparable present value”). The subject present value is then roughly calculated by multiplying the comparable present value of each comparable property by the fractional ratio of the subject size (e.g., the square metre floor area of the subject property) divided by the comparable size (e.g., square metre floor area of the comparable property) and averaging the results.

While the collection of professional appraisals for subject properties is a best practice, it is time consuming, relatively expensive, and restricted to a calculation of subject present value. What is needed in an evaluation platform for loan requests capable of a faster evaluation of the subject property that is less expensive and not limited to subject present value. The range of future value of a subject property (hereafter, “the subject future value range”) is of particular business value because the loan is secured not by the subject present value of the subject property, but rather by the subject future value range of the subject property.

Statistics reveal that the retention of fair market value and possible appreciation of fair market value of real property, as represented by a future value range, varies widely. Variance of the future value range of subject properties can be associated with differences in the subject details between the subject properties, such as: subject type (e.g., freehold, leasehold, landed, condominium, and attached residence), subject proximity to local landmarks (e.g., distance of the subject property from a local hospital, a local school, a public park, a local retail shopping area, or public transportation hubs), subject floor level (e.g., floor level of the subject property in a larger structure), subject postal code (e.g., physical address of the subject property), subject size, and a subject completion date (e.g., year of completion of building of the subject property’s structure).

In short, some subject properties will be better able to retain their subject present value and even appreciate in value over the decades of a loan, while other subject properties may diminish in value over the decades. To effectively sort through loan requests for subject properties, an improved evaluation platform and evaluation method is needed to receive the loan requests for the subject properties, quickly identify comparable properties for each of the subject properties, and determine a subject future value range of each of the subject properties.

Additional, as risk tolerances and business models vary between investors, it is additionally advantageous to create an evaluation platform and evaluation method that is capable of qualifying incoming loan requests based on the different qualification profiles of different investors and then automatically deliver a qualification package of relevant information.

SUMMARY

A general embodiment of the invention is evaluation platform or an evaluation method for receiving and evaluating each of a plurality of loan requests for subject property. A data aggregator aggregates subject details of the subject property and comparable details of comparable properties to calculate a normalized present value and a normalized future value range of each of the comparable properties with the assistance of raw data. The raw data includes property transaction information, geospatial data, macroeconomic data, and census data. The normalized present values and normalized future value ranges are used to calculate a subject present value and a subject future value range for the subject property, which are matched with qualification profiles of investors for delivery of qualification packages to the investors according to a delivery protocol for each qualification match.

A first embodiment of the invention is an evaluation platform for receiving and evaluating a plurality of loan requests, the platform comprising: (a) a web server configured to receive each of the loan requests; (b) a data aggregator; (c) a valuation module; (d) a qualification module; and (e) an output module.

In the first embodiment of the invention, the web server is data communication with with a plurality of computing devices via a network. Each computing device includes a user interface for presenting an entry portal supported by the web server. The entry portal supports data entry via the user interface by a customer to capture customer information for each loan request, the customer information including: (1) biographical information for the customer; (2) a subject address for a subject property associated with the loan request; and (3) a title status for the subject property. The web server is configured to store the customer information for each loan request in one of a plurality of request records in a request database.

In the first embodiment of the invention, the data aggregator is configured to, for each loan request: (i) access a first set of raw data and a second set of raw data from a plurality of raw data servers via the network; (ii) aggregate subject details for the subject property of the loan request; (iii) aggregate, for a plurality of sample properties, a plurality of comparable details; and (iv) select a plurality of comparable properties for the subject property for the loan request from the sample properties. The first set of raw data includes: (a) property transaction data; and (b) geospatial data. The second set of raw data includes: (a) macroeconomic data; and (b) census data. The subject details include subject type, subject proximity to local landmarks, subject floor level, subject postal code, subject size, a subject completion date, a subject purchase price, and subject purchase date. The subject details are aggregated from: (a) the subject address of the subject property; and (b) the first set of raw data. The comparable details of each of the sample properties include a comparable type, a comparable proximity to local landmarks, a comparable floor level, a comparable postal code, a comparable size, a comparable completion date, a comparable purchase price, and a comparable purchase price. The comparable details of each sample property of the sample properties are aggregated from the first set of raw data. The selection of the comparable properties is based on a first comparable scoring between, for each sample property, each comparable detail of the sample property and the subject detail corresponding to the comparable detail of the sample property. Each of the subject details are associated with one of a plurality of first weightage factors in the first comparable scoring.

In the first embodiment of the invention, the valuation module is configured to, for each loan request: (i) calculate for each comparable property a comparable present value; (ii) calculate for each comparable property a normalized present value; (iii) calculate a subject present value of the subject property; (iv) calculate for each comparable property a normalized future value range; and (v) calculate a subject future value range of the subject property. The comparable present value of each comparable property is calculated from: (1) the comparable purchase price of the comparable property; (2) the comparable purchase date of the comparable property; and (3) the second set of raw data. The normalized present value of each comparable property is calculated from: (a) the comparable present value of the comparable property; and (b) a second comparable scoring between each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property. Each of the subject details are associated with one of a plurality of second weightage factors in the second comparable scoring. The subject present value of the subject property is calculated from: (a) the normalized present value of each of the comparable properties; and (b) a third comparable scoring between, for each comparable property, each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property. Each of the subject details are associated with one of a plurality of third weightage factors in the third comparable scoring. The normalized future value range of each comparable property is calculated from: (a) the normalized present value of the comparable property; and (b) the second set of raw data. The normalized future value range includes at least one of an upper bound, a median bound, and a lower bound. The subject future value range of the subject property is calculated from: (a) the normalized future value range of each of the comparable properties; and (b) a fourth comparable scoring between, for each comparable property, each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property. Each of the subject details are associated with one of a plurality of fourth weightage factors in the fourth comparable scoring.

In the first embodiment of the invention, the qualification module is configured to: (i) link to one or more investor servers via the network; (ii) access a plurality of qualification profiles from the investor servers; and (iii) identify one or more qualification matches for each loan request. Each qualification profile includes: (1) an investment program identifier; (2) a delivery protocol; and (3) a plurality of qualification criteria including a qualification present value criteria and a qualification future value criteria. The identification of the one or more qualifications matches includes comparing each of the qualification profiles against: (a) the subject present value of the subject property; (b) the subject future value range of the subject property; and (c) at least one of the subject details of the subject property. For each qualification match of the loan request: (a) the subject present value of the subject property meets the qualification present value criteria of the qualification profile associated with the qualification match; and (b) the subject future value range of the subject property meets the qualification future value criteria of the qualification profile associated with the qualification match.

In the first embodiment of the invention, the output module is configured to, for each of the qualification matches of each loan request, deliver a qualification package. Each of the qualification packages is delivered via the delivery protocol of the qualification profile associated with the qualification match. The qualification package associated with each qualification match is configured to: (1) identify the investment program identifier associated with the qualification profile of the qualification match; and (2) include or link to the subject present value of the loan request and the subject future value range of the loan request. A second embodiment of the invention is a computer-implemented method for receiving and evaluating a plurality of loan requests, the method comprising: (a) receiving at a web server each of the loan requests; (b) linking to one or more investor servers via the network to access a plurality of qualification profiles from the investor servers; (c) performing calculations for each loan request; and (d) for each of the qualification matches of the loan request, delivering a qualification package.

In the second embodiment of the invention, the web server is in data communication with a plurality of computing devices via a network. Each computing device includes a user interface for presenting an entry portal supported by the web server. The entry portal supports data entry via the user interface by a customer to capture customer information for each loan request, the customer information including biographical information for the customer, a subject address for a subject property associated with the loan request, and a title status for the subject property. The web server is configured to store the customer information for each loan request in one of a plurality of request records in a request database.

In the second embodiment of the invention, each qualification profile includes: (i) an investment program identifier; (ii) a delivery protocol; and (iii) a plurality of qualification criteria including a qualification present value criteria and a qualification future value criteria.

In the second embodiment of the invention, the step of performing the calculations for each loan request includes: (i) accessing a first set of raw data and a second set of raw data from a plurality of raw data servers via the network; (ii) aggregating subject details for the subject property of the loan request; (iii) aggregating, for a plurality of sample properties, a plurality of comparable details; (iv) selecting a plurality of comparable properties for the subject property for the loan request from the sample properties; (v) calculating for each comparable property a comparable present value; (vi) calculating for each comparable property a normalized present value; (vii) calculating a subject present value of the subject property; (viii) calculating for each comparable property a normalized future value range; (ix) calculating a subject future value range of the subject property; and (x) identifying one or more qualification matches for the loan request. The first set of raw data includes property transaction data; and geospatial data. The second set of raw data includes macroeconomic data and census data. The subject details include subject type, subject proximity to local landmarks, subject floor level, subject postal code, subject size, a subject completion date, a subject purchase price, and subject purchase date. The subject details are aggregated from the subject address of the subject property and the first set of raw data. The comparable details of each of the sample properties include a comparable type, a comparable proximity to local landmarks, a comparable floor level, a comparable postal code, a comparable size, a comparable completion date, a comparable purchase price, and a comparable purchase price. The comparable details of each sample property of the sample properties are aggregated from the first set of raw data. The selection of the comparable properties is based on a first comparable scoring between, for each sample property, each comparable detail of the sample property and the subject detail corresponding to the comparable detail of the sample property. Each of the subject details are associated with one of a plurality of first weightage factors in the first comparable scoring. The comparable present value of each comparable property is calculated from: (1) the comparable purchase price of the comparable property; (2) the comparable purchase date of the comparable property; and (3) the second set of raw data. The normalized present value of each comparable property is calculated from: (a) the comparable present value of the comparable property; and (b) a second comparable scoring between each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property. Each of the subject details are associated with one of a plurality of second weightage factors in the second comparable scoring. The subject present value of the subject property is calculated from: (a) the normalized present value of each of the comparable properties; and (b) a third comparable scoring between, for each comparable property, each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property. Each of the subject details are associated with one of a plurality of third weightage factors in the third comparable scoring. The normalized future value range of each comparable property is calculated from: (a) the normalized present value of the comparable property; and (b) the second set of raw data. The normalized future value range includes at least one of an upper bound, a median bound, and a lower bound. The subject future value range of the subject property is calculated from: (a) the normalized future value range of each of the comparable properties; and (b) a fourth comparable scoring between, for each comparable property, each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property. Each of the subject details are associated with one of a plurality of fourth weightage factors in the fourth comparable scoring. The identification of the one or more qualifications matches includes comparing each of the qualification profiles against: (a) the subject present value of the subject property; (b) the subject future value range of the subject property; and (c) at least one of the subject details of the subject property. For each qualification match of the loan request: (a) the subject present value of the subject property meets the qualification present value criteria of the qualification profile associated with the qualification match; and (b) the subject future value range of the subject property meets the qualification future value criteria of the qualification profile associated with the qualification match.

In the second embodiment of the invention, each of the qualification packages is delivered via the delivery protocol of the qualification profile associated with the qualification match. The qualification package associated with each qualification match is configured to: (1) identify the investment program identifier associated with the qualification profile of the qualification match; and (2) include or link to the subject present value of the loan request and the subject future value range of the loan request.

The invention has several technical advantages over the prior art. Unlike the collection of professional appraisals, the invention is able to evaluate loan requests in an automated fashion based on consistent metrics. Professional appraisals for subject properties are completed by individuals. While efforts can be made to maintain consistent standards between these individuals, professional experience and inherent biases result inevitably in the inconsistent evaluation of each of the subject properties by the various professional appraisers.

Due to the automated approach of the invention, the invention has a significant economic benefit to those companies using the invention. There is a significant time delay between the request and receipt of a professional appraisal. Customers often seek loan requests from multiple investors (e.g., more than one bank or investment service), and may pursue the loan offer presented to them in the most timely manner. The invention therefore also enables a business advantage of rapid response to potential customers. Another business advantage enabled by the invention is the sorting and routing of loan requests to the most interested investors. The invention can store or link to qualification profiles of investors and then deliver a qualification package for each loan request to one or more suitable potential investors.

The invention also tackles the issue of loan default by calculating the subject future value range of the subject property. Statistical data for subject details can be both evaluated by the invention from multivariate raw data and also applied to the subject future value range calculations. This multivariate evaluation is not performed once, but rather in four separate levels. In each of the four levels, multivariate evaluation is performed between comparable details (of either sample properties or its subset of comparable properties) and the subject details of the subject property. The first level applies this multivariate analysis to identify comparable properties from the sample properties. The second level applies this multivariate analysis to calculate normalized present value of each comparable property. The third level applies this multivariate analysis to calculate subject present value of the subject property. The fourth level applies this multivariate analysis to calculate a subject future value range of the subject property.

Each level of the multilevel, multivariate evaluation has its own comparable scoring based on its own weightage factors. The weightage factors for specific subject details can be different between the first weightage factors, second weightage factors, third weightage factors, and fourth weightage factors. As an example, weightage factors for subject details may differ between: (i) the third weightage factors associated with calculation of subject present value; and (ii) the fourth weightage factors associated with calculation of the subject future value range. A simple example of such a difference in the weightage factors would be for the subject type (e.g., leasehold versus freehold). In the short term (e.g., for present value), the leasehold status may have little impact of the subject present value, however an impending expiration of the lease for the leasehold in a few decades (e.g., near the end of a customer’s retirement) could have significant impact on the subject future value range. The invention, for example, enables heavier weighting of the subject type in the fourth level’s determination of subject future value range.

As each level has independent weightage factors, these weightage factors can be independently calculated or modeled specifically for the applicable determination (e.g., present value versus future value ranges). Hence, the invention enables a segmented multilevel approach, even though much of the same raw data is reused at each level. This segmented multilevel approach lays bare the assumptions made at each level and opens up the platform and method to rapid development, debugging, and adjustment for future changes in business models or the economic status of a locality.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure are described herein with reference to the drawings in which:

FIG. 1 is a block diagram representing an evaluation platform in an evaluation system in an embodiment of the invention.

FIG. 2 is a flowchart representation of a calculation of a first comparable scoring used in selection of comparable properties in an embodiment of the invention.

FIG. 3 is a textual flowchart representation of receiving a plurality of loan requests in an embodiment of the invention.

FIG. 4 is a textual flowchart representation of evaluation of the loan request in an embodiment of the invention.

FIG. 5 is a chart detailing inputs and outputs for comparable scorings in an embodiment of the invention.

DETAILED DESCRIPTION

In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. The illustrative embodiments described in the detailed description, drawings and claims are not meant to be limiting. Other embodiments can be utilized, and other changes can be made, without departing from the spirit or scope of the subject matter presented herein. Unless specified otherwise, the terms “comprising,” “comprise,” “including” and “include” used herein, and grammatical variants thereof, are intended to represent “open” or “inclusive” language such that they include recited elements but also permit inclusion of additional, un-recited elements. As used herein, a server may include a single stand-alone computer, a server, multiple dedicated servers, and/or a virtual server running on a larger network of servers and/or cloud-based service. As used herein, a computing device may include a laptop computer, a desktop computer, a smart phone, a tablet computer, or similar personal computing device. As used herein, a database may store data to and access data from a single stand-alone computer, a data server, multiple dedicated data servers, a cloud-based service, and/or a virtual server running on a larger network of servers.

A general embodiment of the invention is an evaluation platform 11 or an evaluation method for receiving and evaluating each of a plurality of loan requests for subject property. A data aggregator 11C aggregates subject details SDO-N of the subject property and comparable details CDO-N of comparable properties to calculate a normalized present value and a normalized future value range of each of the comparable properties with the assistance of raw data. The raw data includes property transaction information, geospatial data, macroeconomic data, and census data. The normalized present values and normalized future value ranges are used to calculate a subject present value and a subject future value range for the subject property, which are matched with qualification profiles of investors for delivery of qualification packages to the investors according to a delivery protocol for each qualification match. FIG. 1 is a block diagram representing an evaluation platform 11 in an evaluation system 10 in an embodiment of the invention. The evaluation platform 11 includes a web server 11A, a request database 11B, a data aggregator 11C, a valuation module 11D, a qualification module HE, and an output module HF. The evaluation platform 11 is in data communication via a network 12 with a plurality of computing devices 13, a plurality of investor servers 14, and a plurality of raw data servers 15.

FIG. 2 is a flowchart representation of a calculation of a first comparable scoring 23 used in selection of comparable properties 20 in an embodiment of the invention. The selection of the comparable properties 20 commences with access to a plurality of request records 21 and a plurality of raw data servers 15. Each of the request records 21 includes biographical information 21A of a customer, a subject address 21B, and a title status for the subject property. The raw data servers 15 include a first set of raw data 15A and a second set of raw data 15B. While not illustrated, the first set of raw data 15A may be distributed between a first subset of a plurality of the raw data servers 15, also the second set of raw data 15B may be distributed between a second subset of a plurality of the raw data servers 15. Subject details SDO-N for each request record 21 of the plurality of request records 21 are aggregated from the subject address 21B of the request record 21 and the first set of raw data 15A. Comparable details CDO-N for each sample property of a plurality of sample properties are aggregated from the first set of raw data 15A.

As illustrated in FIG. 2, each comparable detail CDN of a sample property is compared against the subject detail SDN associated with the comparable detail CDN. Each comparison is given one of a plurality of first weightage factors 1WO-N associated with the subject detail SDN associated with the first weightage factor IWN. For example, as illustrated in FIG. 2: (i) subject detail SDo of the subject property is compared against comparable detail CDo for a given sample property, and this comparison is given the first weightage factor IWo; (ii) subject detail SDi of the subject property is compared against comparable detail CDi for the given sample property, and this comparison is given the first weightage factor IWi; and (iii) subject detail SDN of the subject property is compared against comparable detail CDN for the given sample property, and this comparison is given the first weightage factor IWN. A first comparable scoring 23 between the subject property and the given sample property is calculated from a combination of each of the first weighted comparisons. The first comparable scoring 23 can be a simple summation of each of the first weighted comparisons, a multiplication of each of the first weighted comparisons, a geometric summation of each of the first weighted comparisons, a logarithmic summation of each of the first weighted comparisons, or other algebraic combination.

While not illustrated in FIG. 2, the selection of comparable properties 20 from the sample properties can include multiple iterations of sub-selections to reduce the number of detailed comparisons to be made by the data aggregator 11C of the evaluation platform 11. For instance, initial sub-selections of sample properties may be first made for each sample property by comparable postal code comparisons, comparable size comparisons (e.g., square metre range), comparable type comparisons (e.g., freehold, leasehold, landed, condominium, or attached residence), comparable completion date comparisons, or comparable purchase price range comparisons. For example, after an initial sub- selections of sample properties, these sample properties can be subjected to additional comparisons such by comparable proximity to local landmarks or by comparable floor level.

FIG. 3 is a textual flowchart 3-00 representation of receiving a plurality of loan requests in an embodiment of the invention. The flowchart 3-00 illustrates the steps 3-01 to 3-03 (see below) for implementing an embodiment of the invention.

3-01 web server 11A hosts entry portal on the user interface of each of a plurality of computing devices 13 via a network 12

3-02 customer enters customer information using the entry portal, the customer informationfor the loan request including:

• biographical information 21A

• a subject address 21B

• a title status

3-03 web server 11A receives loan request and stores the customer information of each loan request into a request record 21 of a request database 11B FIG. 4 is a textual flowchart 4-00 representation of evaluation of the loan request in an embodiment of the invention. The flowchart 4-00 illustrates the steps 4-01 to 4-03 (see below) for implementing an embodiment of the invention.

4-01 receive at the web server 11A each of the loan requests

4-02 link to one or more investor servers 14 via the network 12 to access a plurality of qualification profiles

4-03 for each loan request:

4-03a • access a first set of raw data 15A and a second set of raw data 15B from a plurality of raw data servers 15

4-03b • aggregate subject details SDO-N for the subject property and comparable details CDO-N for a plurality of sample properties

4-03c • select a plurality of comparable properties for the subject property

4-03d • calculate a normalized present value and a normalized future value range for each comparable property

4-03e • calculate for the subject property a subject present value and a subject future value range

4-03f • identify one or more qualifying matches with the qualification criteria of the qualification profiles

4-03g • delivery a qualification package for each qualification match via the delivery protocol of the associated qualification profile

FIG. 5 is a chart detailing inputs and outputs for comparable scorings in an embodiment of the invention. The chart includes four rows, one row for each of: a first comparable scoring 23, a second comparable scoring, a third comparable scoring, and a fourth comparable scoring.

The first comparable scoring 23 detailed in the first row of the FIG. 5 chart 5-00 details that the first row output includes a plurality of comparable properties selected from a plurality of sample properties. The inputs include the comparable details CDO-N of the sample properties and the first weightage factors 1WO-N. Each of the comparable details CDO-N of each of the sample properties is compared against the subject detail SDN corresponding to the comparable detail CDN of the sample property to execute the first comparable scoring 23. For example for a given sample property, the comparable postal code would be compared against the subject postal code of the subject property. Similarly, for the given property, the comparable size of the sample property would be compared against the subject size of the subject property. This process is repeated for a plurality of sample properties.

As detailed in FIG. 2, each of the subject details SDO-N has an associated first weightage factor. In this manner, a specific subject detail SDN such as the subject size can be given a larger impact in the first comparable scoring 23 than another specific subject detail SDN such as the subject floor level. While not illustrated, each of the first weightage factors 1WO-N can be a predetermined weightage factor or a modeled weightage factor.

Note that the selection of comparable properties 20 will be a subset of the sample properties. In this manner, the evaluation platform 11 can employed to evaluate a wide variety of subject properties. For instance, the same or similar set of sample properties can be employed in the valuation of subject properties from different areas of a city or country (e.g., with different subject postal codes) and subject properties with different subject types (e.g., such as freehold, leasehold, landed, condominium, and attached residence).

The second comparable scoring detailed in the second row of the FIG. 5 chart 5-00 details that the second row output includes a normalized present value (PV) of each comparable property of the comparable properties. The inputs include the comparable present values of the comparable properties and the second weightage factors. Each of the comparable details CDO-N of each of the comparable properties is compared against the subject detail SDN corresponding to the comparable detail CDN of the comparable property to execute the second comparable scoring.

As with the first comparable scoring 23, because each subject detail SDN has a specific second weightage factor, the comparison of some of the specific subject details SDO-N will have greater impact in the second comparable scoring than the comparison of other specific subject details SDO-N. In this manner, the comparable present value of each comparable property is increased or decreased to “normalize” it to the subject details SDO-N of the subject property.

The third comparable scoring detailed in the third row of the FIG. 5 chart 5-00 details that the third row output includes a subject present value of the subject property. The inputs include the normalized present values of the comparable properties and the third weightage factors. Each of the comparable details CDO-N of each of the comparable properties is compared against the subject detail SDN corresponding to the comparable detail CDN of the comparable property to execute the third comparable scoring.

As with the first comparable scoring 23 and the second comparable scoring, because each subject detail SDN has a specific third weightage factor, the comparison of some of the specific subject details SDO-N will have greater impact in the third comparable scoring than the comparison of other specific subject details SDO-N. In this manner, comparable properties that are more aligned with specific subject details SDO-N of the subject property will be given more weight in the determination of the subject present value than other comparable properties that are more aligned with other specific subject details SDO-N.

The fourth comparable scoring detailed in the fourth row of the FIG. 5 chart 5-00 details that the fourth row output includes a subject future value (FV) range of the subject property. The inputs include the normalized future values of the comparable properties and the fourth weightage factors. Each of the comparable details CDO-N of each of the comparable properties is compared against the subject detail SDN corresponding to the comparable detail CDN of the comparable property to execute the fourth comparable scoring.

As with the first comparable scoring 23, the second comparable scoring, and the third comparable scoring, because each subject detail SDN has a specific fourth weightage factor, the comparison of some of the specific subject details SDO-N will have greater impact in the fourth comparable scoring than the comparison of other specific subject details SDO-N. In this manner, comparable properties that are more aligned with specific subject details SDO-N of the subject property will be given more weight in the determination of the subject future value range than other comparable properties that are more aligned with other specific subject details SDO-N. A general embodiment of the invention is evaluation platform 11 or an evaluation method for receiving and evaluating each of a plurality of loan requests for subject property. A data aggregator 11C aggregates subject details SDO-N of the subject property and comparable details CDO-N of comparable properties to calculate a normalized present value and a normalized future value range of each of the comparable properties with the assistance of raw data. The raw data includes property transaction information, geospatial data, macroeconomic data, and census data. The normalized present values and normalized future value ranges are used to calculate a subject present value and a subject future value range for the subject property, which are matched with qualification profiles of investors for delivery of qualification packages to the investors according to a delivery protocol for each qualification match.

A first embodiment of the invention is an evaluation platform 11 for receiving and evaluating a plurality of loan requests, the platform comprising: (a) a web server 11A configured to receive each of the loan requests; (b) a data aggregator 11C; (c) a valuation module 11D; (d) a qualification module HE; and (e) an output module HF.

In the first embodiment of the invention, the web server HA is data communication with with a plurality of computing devices 13 via a network 12. Each computing device 13 includes a user interface for presenting an entry portal supported by the web server HA. The entry portal supports data entry via the user interface by a customer to capture customer information for each loan request, the customer information including: (1) biographical information 21A for the customer; (2) a subject address 21B for a subject property associated with the loan request; and (3) a title status for the subject property. The web server HA is configured to store the customer information for each loan request in one of a plurality of request records 21 in a request database 11B.

In the first embodiment of the invention, the data aggregator 11C is configured to, for each loan request: (i) access a first set of raw data 15A and a second set of raw data 15B from a plurality of raw data servers 15 via the network 12; (ii) aggregate subject details SDO-N for the subject property of the loan request; (iii) aggregate, for a plurality of sample properties, a plurality of comparable details CDO-N; and (iv) select a plurality of comparable properties for the subject property for the loan request from the sample properties. The first set of raw data 15A includes: (a) property transaction data; and (b) geospatial data. The second set of raw data 15B includes: (a) macroeconomic data; and (b) census data. The subject details SDO-N include subject type, subject proximity to local landmarks, subject floor level, subject postal code, subject size, a subject completion date, a subject purchase price, and subject purchase date. The subject details SDO-N are aggregated from: (a) the subject address 21B of the subject property; and (b) the first set of raw data 15A. The comparable details CDO-N of each of the sample properties include a comparable type, a comparable proximity to local landmarks, a comparable floor level, a comparable postal code, a comparable size, a comparable completion date, a comparable purchase price, and a comparable purchase price. The comparable details CDO-N of each sample property of the sample properties are aggregated from the first set of raw data 15A. The selection of the comparable properties 20 is based on a first comparable scoring 23 between, for each sample property, each comparable detail CDN of the sample property and the subject detail SDN corresponding to the comparable detail CDN of the sample property. Each of the subject details SDO-N are associated with one of a plurality of first weightage factors 1WO-N in the first comparable scoring 23.

In the first embodiment of the invention, the valuation module 11D is configured to, for each loan request: (i) calculate for each comparable property a comparable present value; (ii) calculate for each comparable property a normalized present value; (iii) calculate a subject present value of the subject property; (iv) calculate for each comparable property a normalized future value range; and (v) calculate a subject future value range of the subject property. The comparable present value of each comparable property is calculated from: (1) the comparable purchase price of the comparable property; (2) the comparable purchase date of the comparable property; and (3) the second set of raw data 15B. The normalized present value of each comparable property is calculated from: (a) the comparable present value of the comparable property; and (b) a second comparable scoring between each of the comparable details CDO-N of the comparable property and the subject detail SDN corresponding to the comparable detail CDN of the comparable property. Each of the subject details SDO-N are associated with one of a plurality of second weightage factors in the second comparable scoring. The subject present value of the subject property is calculated from: (a) the normalized present value of each of the comparable properties; and (b) a third comparable scoring between, for each comparable property, each of the comparable details CDO-N of the comparable property and the subject detail SDN corresponding to the comparable detail CDN of the comparable property. Each of the subject details SDO-N are associated with one of a plurality of third weightage factors in the third comparable scoring. The normalized future value range of each comparable property is calculated from: (a) the normalized present value of the comparable property; and (b) the second set of raw data 15B. The normalized future value range includes at least one of an upper bound, a median bound, and a lower bound. The subject future value range of the subject property is calculated from: (a) the normalized future value range of each of the comparable properties; and (b) a fourth comparable scoring between, for each comparable property, each of the comparable details CDO-N of the comparable property and the subject detail SDN corresponding to the comparable detail CDN of the comparable property. Each of the subject details SDO-N are associated with one of a plurality of fourth weightage factors in the fourth comparable scoring.

In the first embodiment of the invention, the qualification module HE is configured to: (i) link to one or more investor servers 14 via the network 12; (ii) access a plurality of qualification profiles from the investor servers 14; and (iii) identify one or more qualification matches for each loan request. Each qualification profile includes: (1) an investment program identifier; (2) a delivery protocol; and (3) a plurality of qualification criteria including a qualification present value criteria and a qualification future value criteria. The identification of the one or more qualifications matches includes comparing each of the qualification profiles against: (a) the subject present value of the subject property; (b) the subject future value range of the subject property; and (c) at least one of the subject details SDO-N of the subject property. For each qualification match of the loan request: (a) the subject present value of the subject property meets the qualification present value criteria of the qualification profile associated with the qualification match; and (b) the subject future value range of the subject property meets the qualification future value criteria of the qualification profile associated with the qualification match.

In the first embodiment of the invention, the output module HF is configured to, for each of the qualification matches of each loan request, deliver a qualification package. Each of the qualification packages is delivered via the delivery protocol of the qualification profile associated with the qualification match. The qualification package associated with each qualification match is configured to: (1) identify the investment program identifier associated with the qualification profile of the qualification match; and (2) include or link to the subject present value of the loan request and the subject future value range of the loan request.

A second embodiment of the invention is a computer-implemented method for receiving and evaluating a plurality of loan requests, the method comprising: (a) receiving at a web server 11A each of the loan requests; (b) linking to one or more investor servers 14 via the network 12 to access a plurality of qualification profiles from the investor servers 14; (c) performing calculations for each loan request; and (d) for each of the qualification matches of the loan request, delivering a qualification package.

In the second embodiment of the invention, the web server 11A is in data communication with a plurality of computing devices 13 via a network 12. Each computing device 13 includes a user interface for presenting an entry portal supported by the web server 11A. The entry portal supports data entry via the user interface by a customer to capture customer information for each loan request, the customer information including biographical information 21A for the customer, a subject address 21B for a subject property associated with the loan request, and a title status for the subject property. The web server 11A is configured to store the customer information for each loan request in one of a plurality of request records 21 in a request database 11B.

In the second embodiment of the invention, the step of performing the calculations for each loan request includes: (i) accessing a first set of raw data 15A and a second set of raw data 15B from a plurality of raw data servers 15 via the network 12; (ii) aggregating subject details SDO-N for the subject property of the loan request; (iii) aggregating, for a plurality of sample properties, a plurality of comparable details CDO-N; (iv) selecting a plurality of comparable properties for the subject property for the loan request from the sample properties; (v) calculating for each comparable property a comparable present value; (vi) calculating for each comparable property a normalized present value; (vii) calculating a subject present value of the subject property; (viii) calculating for each comparable property a normalized future value range; (ix) calculating a subject future value range of the subject property; and (x) identifying one or more qualification matches for the loan request. The first set of raw data 15A includes property transaction data; and geospatial data. The second set of raw data 15B includes macroeconomic data and census data. The subject details SDO-N include subject type, subject proximity to local landmarks, subject floor level, subject postal code, subject size, a subject completion date, a subject purchase price, and subject purchase date. The subject details SDO-N are aggregated from the subject address 21B of the subject property and the first set of raw data 15A. The comparable details CDO-N of each of the sample properties include a comparable type, a comparable proximity to local landmarks, a comparable floor level, a comparable postal code, a comparable size, a comparable completion date, a comparable purchase price, and a comparable purchase price. The comparable details CDO-N of each sample property of the sample properties are aggregated from the first set of raw data 15A. The selection of the comparable properties 20 is based on a first comparable scoring 23 between, for each sample property, each comparable detail CDN of the sample property and the subject detail SDN corresponding to the comparable detail CDN of the sample property. Each of the subject details SDO-N are associated with one of a plurality of first weightage factors 1WO-N in the first comparable scoring 23. The comparable present value of each comparable property is calculated from: (1) the comparable purchase price of the comparable property; (2) the comparable purchase date of the comparable property; and (3) the second set of raw data 15B. The normalized present value of each comparable property is calculated from: (a) the comparable present value of the comparable property; and (b) a second comparable scoring between each of the comparable details CDO-N of the comparable property and the subject detail SDN corresponding to the comparable detail CDN of the comparable property. Each of the subject details SDO-N are associated with one of a plurality of second weightage factors in the second comparable scoring. The subject present value of the subject property is calculated from: (a) the normalized present value of each of the comparable properties; and (b) a third comparable scoring between, for each comparable property, each of the comparable details CDO-N of the comparable property and the subject detail SDN corresponding to the comparable detail CDN of the comparable property. Each of the subject details SDO-N are associated with one of a plurality of third weightage factors in the third comparable scoring. The normalized future value range of each comparable property is calculated from: (a) the normalized present value of the comparable property; and (b) the second set of raw data 15B. The normalized future value range includes at least one of an upper bound, a median bound, and a lower bound. The subject future value range of the subject property is calculated from: (a) the normalized future value range of each of the comparable properties; and (b) a fourth comparable scoring between, for each comparable property, each of the comparable details CDO-N of the comparable property and the subject detail SDN corresponding to the comparable detail CDN of the comparable property. Each of the subject details SDO-N are associated with one of a plurality of fourth weightage factors in the fourth comparable scoring. The identification of the one or more qualifications matches includes comparing each of the qualification profiles against: (a) the subject present value of the subject property; (b) the subject future value range of the subject property; and (c) at least one of the subject details SDO-N of the subject property. For each qualification match of the loan request: (a) the subject present value of the subject property meets the qualification present value criteria of the qualification profile associated with the qualification match; and (b) the subject future value range of the subject property meets the qualification future value criteria of the qualification profile associated with the qualification match.

In an alternate design of the first and second embodiment of the invention, the second weightage factors are calculated from the first weightage factors 1WO-N of the first comparable scoring 23.

In an alternate design of the first and second embodiment of the invention, the third weightage factors are calculated from at least one of: (a) the first weightage factors 1WO-N of the first comparable scoring 23; and (b) the second weightage factors of the second comparable scoring. In an alternate design of the first and second embodiment of the invention, the fourth weightage factors are calculated from at least one of: (a) the first weightage factors 1WO-N of the first comparable scoring 23; (b) the second weightage factors of the second comparable scoring; and (c) the third weightage factors of the third comparable scoring.

In an alternate design of the first and second embodiment of the invention, the first weightage factors IWO-N, second weightage factors, third weightage factors, and fourth weightage factors are each at least one of: (a) a plurality of predetermined weightage factors; and (b) a plurality of modeled weightage factors determined by creation of at least one Al / ML model.

In an alternate design of the first and second embodiment of the invention, the delivery protocol of each qualification profile includes delivery via an API interface of the investor server 14 associated with the qualification profile.

In an alternate design of the first and second embodiment of the invention: (a) the subject proximity to local landmarks includes a set of distances between the subject address 21B and at least one of a local hospital, a local school, a public park, a local retail shopping area, and one of more public transportation hubs; and (b) the subject type includes at least one of freehold, leasehold, landed, condominium, and attached residence.

In an alternate design of the first and second embodiment of the invention: (a) the biographical information includes an age of the customer and a gender of the customer; (b) the title status for the subject property includes at least one of solely owned by the customer and jointly owned or co-owned by the customer and at least one additional person; (c) the macroeconomic data includes historic interest rates, history equity return rates, historic inflation rates, historic GDP rates; and (d) the census data includes population density, migration growth data, median neighborhood income, and vacancy rates.

In an alternate design of the first and second embodiment of the invention, the sbs CDO-N of each comparable property further includes historic rental income for the comparable property. In an alternate design of the first and second embodiment of the invention, the second set of raw data 15B includes probabilistic data for flood damage, wind damage, earthquake damage, fire, and political stability. While various aspects and embodiments have been disclosed herein, it will be apparent that various other modifications and adaptations of the invention will be apparent to the person skilled in the art after reading the foregoing disclosure without departing from the spirit and scope of the invention and it is intended that all such modifications and adaptations come within the scope of the appended claims. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit of the invention being indicated by the appended claims.

Claims

1. An evaluation platform for receiving and evaluating a plurality of loan requests, the platform comprising:

(a) a web server configured to receive each of the loan requests,

(i) wherein the web server is in data communication with a plurality of computing devices via a network;

(ii) wherein each computing device includes a user interface for presenting an entry portal supported by the web server;

(iii) wherein the entry portal supports data entry via the user interface by a customer to capture customer information for each loan request, the customer information including:

(1) biographical information for the customer;

(2) a subject address for a subject property associated with the loan request; and

(3) a title status for the subject property; and

(iv) wherein the web server is configured to store the customer information for each loan request in one of a plurality of request records in a request database;

(b) a data aggregator configured to, for each loan request:

(i) access a first set of raw data and a second set of raw data from a plurality of raw data servers via the network,

(1) wherein the first set of raw data includes:

(a) property transaction data; and

(b) geospatial data; and

(2) wherein the second set of raw data includes:

(a) macroeconomic data; and

(b) census data;

(ii) aggregate subject details for the subject property of the loan request,

(1) wherein the subject details include subject type, subject proximity to local landmarks, subject floor level, subject postal code, subject size, a subject completion date, a subject purchase price, and subject purchase date; and

(2) wherein the subject details are aggregated from:

27 (a) the subject address of the subject property; and

(b) the first set of raw data;

(iii) aggregate, for a plurality of sample properties, a plurality of comparable details,

(1) wherein the comparable details of each of the sample properties include a comparable type, a comparable proximity to local landmarks, a comparable floor level, a comparable postal code, a comparable size, a comparable completion date, a comparable purchase price, and a comparable purchase price; and

(2) wherein the comparable details of each sample property of the sample properties are aggregated from the first set of raw data; and

(iv) select a plurality of comparable properties for the subject property for the loan request from the sample properties,

(1) wherein the selection of the comparable properties is based on a first comparable scoring between, for each sample property, each comparable detail of the sample property and the subject detail corresponding to the comparable detail of the sample property; and

(2) wherein each of the subject details are associated with one of a plurality of first weightage factors in the first comparable scoring;

(c) a valuation module configured to, for each loan request:

(i) calculate for each comparable property a comparable present value from:

(1) the comparable purchase price of the comparable property;

(2) the comparable purchase date of the comparable property; and

(3) the second set of raw data;

(ii) calculate for each comparable property a normalized present value,

(1) wherein the normalized present value of each comparable property is calculated from:

(a) the comparable present value of the comparable property; and

(b) a second comparable scoring between each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property; and

(2) wherein each of the subject details are associated with one of a plurality of second weightage factors in the second comparable scoring; (iii) calculate a subject present value of the subject property,

(1) wherein the subject present value of the subject property is calculated from:

(a) the normalized present value of each of the comparable properties; and

(b) a third comparable scoring between, for each comparable property, each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property; and

(2) wherein each of the subject details are associated with one of a plurality of third weightage factors in the third comparable scoring;

(iv) calculate for each comparable property a normalized future value range,

(1) wherein the normalized future value range of each comparable property is calculated from:

(a) the normalized present value of the comparable property; and

(b) the second set of raw data; and

(2) wherein the normalized future value range includes at least one of an upper bound, a median bound, and a lower bound; and

(v) calculate a subject future value range of the subject property,

(1) wherein the subject future value range of the subject property is calculated from:

(a) the normalized future value range of each of the comparable properties; and

(b) a fourth comparable scoring between, for each comparable property, each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property; and

(2) wherein each of the subject details are associated with one of a plurality of fourth weightage factors in the fourth comparable scoring; (d) a qualification module configured to:

(i) link to one or more investor servers via the network;

(ii) access a plurality of qualification profiles from the investor servers, wherein each qualification profile includes:

( 1 ) an investment program identifier;

(2) a delivery protocol; and

(3) a plurality of qualification criteria including:

(a) a qualification present value criteria; and

(b) a qualification future value criteria; and

(iii) identify one or more qualification matches for each loan request,

(1) wherein the identification of the one or more qualifications matches includes comparing each of the qualification profiles against:

(a) the subject present value of the subject property;

(b) the subject future value range of the subject property; and

(c) at least one of the subject details of the subject property; and

(2) wherein, for each qualification match of the loan request:

(a) the subject present value of the subject property meets the qualification present value criteria of the qualification profile associated with the qualification match; and

(b) the subject future value range of the subject property meets the qualification future value criteria of the qualification profile associated with the qualification match; and

(e) an output module configured to, for each of the qualification matches of each loan request, deliver a qualification package,

(i) wherein each of the qualification packages is delivered via the delivery protocol of the qualification profile associated with the qualification match; and

(ii) wherein the qualification package associated with each qualification match is configured to:

(1) identify the investment program identifier associated with the qualification profile of the qualification match; and

(2) include or link to:

(a) the subject present value of the loan request; and

(b) the subject future value range of the loan request.

2. The platform of claim 1, wherein the second weightage factors are calculated from the first weightage factors of the first comparable scoring.

3. The platform of claim 1, wherein the third weightage factors are calculated from at least one of:

(a) the first weightage factors of the first comparable scoring; and

(b) the second weightage factors of the second comparable scoring.

4. The platform of claim 1, wherein the fourth weightage factors are calculated from at least one of:

(a) the first weightage factors of the first comparable scoring;

(b) the second weightage factors of the second comparable scoring; and

(c) the third weightage factors of the third comparable scoring.

5. The platform of claim 1, wherein the first weightage factors, second weightage factors, third weightage factors, and fourth weightage factors are each at least one of:

(a) a plurality of predetermined weightage factors; and

(b) a plurality of modeled weightage factors determined by creation of at least one Al / ML model.

6. The platform of claim 1, wherein the delivery protocol of each qualification profile includes delivery via an API interface of the investor server associated with the qualification profile.

7. The platform of claim 1:

(a) wherein the subject proximity to local landmarks includes a set of distances between the subject address and at least one of:

(i) a local hospital;

(ii) a local school;

(iii) a public park;

(iv) a local retail shopping area; and

31 (v) one of more public transportation hubs; and

(b) wherein the subject type includes at least one of freehold, leasehold, landed, condominium, and attached residence.

8. The platform of claim 1,

(a) wherein the biographical information includes:

(i) an age of the customer; and

(ii) a gender of the customer;

(b) wherein the title status for the subject property includes at least one of:

(i) solely owned by the customer; and

(ii) jointly owned or co-owned by the customer and at least one additional person;

(c) wherein the macroeconomic data includes:

(i) historic interest rates;

(ii) history equity return rates;

(iii) historic inflation rates; and

(iv) historic GDP rates; and

(d) wherein the census data includes:

(i) population density;

(ii) migration growth data;

(iii) median neighborhood income; and

(iv) vacancy rates.

9. The platform of claim 1, wherein the comparable details of each comparable property further includes historic rental income for the comparable property.

10. The platform of claim 1, wherein the second set of raw data includes probabilistic data for flood damage, wind damage, earthquake damage, fire, and political stability.

32

11. A computer- implemented method for receiving and evaluating a plurality of loan requests, the method comprising:

(a) receiving at a web server each of the loan requests,

(1) biographical information for the customer;

(3) a title status for the subject property; and

(b) linking to one or more investor servers via the network to access a plurality of qualification profiles from the investor servers, wherein each qualification profile includes:

(i) an investment program identifier;

(ii) a delivery protocol; and

(iii) a plurality of qualification criteria including:

(1) a qualification present value criteria; and

(2) a qualification future value criteria;

(c) performing calculations for each loan request, wherein performing the calculations for each loan request includes:

(i) accessing a first set of raw data and a second set of raw data from a plurality of raw data servers via the network,

(1) wherein the first set of raw data includes:

(a) property transaction data; and

(b) geospatial data; and

(2) wherein the second set of raw data includes:

33 (a) macroeconomic data; and

(b) census data;

(ii) aggregating subject details for the subject property of the loan request,

(2) wherein the subject details are aggregated from:

(a) the subject address of the subject property; and

(b) the first set of raw data;

(iii) aggregating, for a plurality of sample properties, a plurality of comparable details,

(2) wherein the comparable details of each sample property of the sample properties are aggregated from the first set of raw data;

(iv) selecting a plurality of comparable properties for the subject property for the loan request from the sample properties,

(v) calculating for each comparable property a comparable present value from:

(1) the comparable purchase price of the comparable property;

(2) the comparable purchase date of the comparable property; and

(3) the second set of raw data;

34 (vi) calculating for each comparable property a normalized present value,

(a) the comparable present value of the comparable property; and

(2) wherein each of the subject details are associated with one of a plurality of second weightage factors in the second comparable scoring;

(vii) calculating a subject present value of the subject property,

(a) the normalized present value of each of the comparable properties; and

(viii) calculating for each comparable property a normalized future value range,

(a) the normalized present value of the comparable property; and

(b) the second set of raw data; and

(2) wherein the normalized future value range includes at least one of an upper bound, a median bound, and a lower bound;

(ix) calculating a subject future value range of the subject property,

(a) the normalized future value range of each of the comparable properties; and

35 (b) a fourth comparable scoring between, for each comparable property, each of the comparable details of the comparable property and the subject detail corresponding to the comparable detail of the comparable property; and

(2) wherein each of the subject details are associated with one of a plurality of fourth weightage factors in the fourth comparable scoring; and

(x) identifying one or more qualification matches for the loan request,

(a) the subject present value of the subject property;

(b) the subject future value range of the subject property; and

(c) at least one of the subject details of the subject property; and

(2) wherein, for each qualification match of the loan request:

(d) for each of the qualification matches of the loan request, delivering a qualification package,

(2) include or link to:

(a) the subject present value of the loan request; and

(b) the subject future value range of the loan request.

36

12. The method of claim 11, wherein the second weightage factors are calculated from the first weightage factors of the first comparable scoring.

13. The method of claim 11, wherein the third weightage factors are calculated from at least one of:

(a) the first weightage factors of the first comparable scoring; and

(b) the second weightage factors of the second comparable scoring.

14. The method of claim 11, wherein the fourth weightage factors are calculated from at least one of:

(a) the first weightage factors of the first comparable scoring;

(b) the second weightage factors of the second comparable scoring; and

(c) the third weightage factors of the third comparable scoring.

15. The method of claim 11, wherein the first weightage factors, second weightage factors, third weightage factors, and fourth weightage factors are each at least one of:

(a) a plurality of predetermined weightage factors; and

16. The method of claim 11, wherein the delivery protocol of each qualification profile includes delivery via an API interface of the investor server associated with the qualification profile.

17. The method of claim 11 :

(i) a local hospital;

(ii) a local school;

(iii) a public park;

(iv) a local retail shopping area; and

(v) one of more public transportation hubs; and

37 (b) wherein the subject type includes at least one of freehold, leasehold, landed, condominium, and attached residence.

18. The method of claim 11 ,

(a) wherein the biographical information includes:

(i) an age of the customer; and

(ii) a gender of the customer;

(b) wherein the title status for the subject property includes at least one of:

(i) solely owned by the customer; and

(c) wherein the macroeconomic data includes:

(i) historic interest rates;

(ii) history equity return rates;

(iii) historic inflation rates; and

(iv) historic GDP rates; and

(d) wherein the census data includes:

(i) population density;

(ii) migration growth data;

(iii) median neighborhood income; and

(iv) vacancy rates.

19. The method of claim 11, wherein the comparable details of each comparable property further includes historic rental income for the comparable property.

20. The method of claim 11, wherein the second set of raw data includes probabilistic data for flood damage, wind damage, earthquake damage, fire, and political stability.

38