AU2016250337A1 - A method and apparatus for facilitating valuation and sale of real estate - Google Patents

Abstract

- 23 A METHOD AND APPARATUS FOR FACILITATING VALUATION AND SALE OF REAL ESTATE The present invention relates to a method and apparatus for facilitating the evaluation and sale of real estate. The method and apparatus are arranged to access a multiplicity of data sources, including socio- economic data, macro - economic trend data and buyer data relating to personal circumstances of potencial buyers of a property. The data can be dynamically 0 processed to provide dynamic updates of real estate prices. Platforms are provided to enable buyers and sellers to contact each other where the seller price and the buyer price match within a given range and there is therefore the potential for a sale. Data values may be obtained automatically by accessing of data which is available on 5 networks, such as social networks. COMMUNICATIONS F- t LO 4) QO o ru 0 r-i U -' z i Zuu 00 fil 0i s - -- 1 SNOIIVJINlV JO , o: m mH 11 'ft 04 Kbr-

Description

-1 - 2016250337 24 Oct 2016

A METHOD AND APPARATUS FOR FACILITATING VALUATION AND SALE OF

REAL ESTATE

Technical Field 5 The present invention relates to a method and apparatus for facilitating the evaluation and sale of real estate and particularly, but not exclusively, to a method and apparatus for obtaining data from multiple data sources and processing the data to provide a value for real estate.

Background Art 0 People purchase real estate for many reasons. For example, people purchase property to live in, as an investment and as a workplace. The cost of purchase of property may require a significant portion of an individual’s wealth. It is important that purchasers obtain just value for their purchase.

Traditional methods of evaluating real estate and establishing a price are not 5 satisfactory. Traditional methods are based on physical attributes, size, amenities, location and similar prices of real estate in the location. It is easy to get the evaluation of real estate wrong. Also, the price may be driven by a real estate agent, not the buyer. This may lead to an unfair sale price.

References in this document to the background art do not constitute an admission that 2 0 the art forms part of the common general knowledge of a person of ordinary skill in the art. Such references are also not intended to limit the application of the method and apparatus as disclosed herein.

Summary

In accordance with a first aspect, the present invention provides a method of 2 5 facilitating the evaluation and sale of real estate, comprising the steps of accessing one or more data sources and obtaining data values for one or more of the following data types: socio-economic data relating to socio-economic factors in a geographical area where a property is located; macro-economic trend data relating to one or more of infrastructure, demographics and political features in a geographical area where the property is located; -2- 2016250337 24 Oct 2016 buyer data relating to personal circumstances of potential buyers of the property; and processing the data values, the processing comprising applying relative weightings to the data values, and processing the weighted data to establish an estimated selling value for the property. 5 In the prior art, traditional information used to value property prices generally comprises the location of the property, and physical attributes of the property. Physical attributes includes features such as number of rooms, number of bedrooms, car spaces, size of land and the like. As discussed above, using this traditional approach can often lead to inaccurate pricing outcomes, which can often be perceived as “unfair” to both buyers and 0 sellers. In this embodiment of the present invention, other, non-traditional data types are used as a basis to establish the value of a property. Advantageously, the use of these non-traditional data types leads to a more accurate pricing result, which, in turn, can facilitate transactions between buyers and sellers.

In an embodiment, the processing is arranged to determine the weighting to apply. In 5 an embodiment, the processing is arranged to infer or determine a variation of the weighting and / or selling value based on the buyer data. The processing therefore inputs a “buyer perspective”. In an embodiment, buyer data may comprise “generic” buyer data, relating to the “type” of buyer, for example. In an embodiment, the buyer data may be data specific to a particular buyer. 0

In an embodiment, the method includes implementing a knowledge based system, which may utilise a a learning algorithm, utilising technologies such as neural networks, for example. The neural network may be initially trained using buyer, seller data and price data.

In an embodiment, data values for the data types are obtained from a plurality of data 2 5 sources. The method may comprise the steps of accessing a plurality of data sources and continually obtaining data, so that large amounts of data are obtained and processed to produce accurate output values for properties. The use of a “big data” approach, obtaining data from a plurality of sources, and obtaining data on data types that are not traditionally used for property valuation, is believed by the applicants to be a comprehensive approach to 3 0 valuing property, which will lead to fairer and trusted outcomes. -3- 2016250337 24 Oct 2016

The data types that may be accessed in this embodiment include:

Socio-economic data. This is data that relates to the socio-economic conditions in the geographical area where the property is located. The “social” aspects may include crime, whether the neighbours are “good” or “bad”, occurrence of social events (which may indicate 5 parking problems, crowd problems, etc.), and any other social factors. Other socio-economic factors may be associated with infrastructure, such as noise, emissions/odour, access to light, infestations, and many others. Data may be obtained from many sources, including social networks, the worldwide web, and other sources. In an embodiment, these socio-economic factors may be classified as either “detractors” (i.e. factors that may tend to lower the price of 0 the property) and “attractors” (factors that may increase the price of the property e.g. good schools in the neighbourhood); macro-economic trend data that relate to infrastructure (e.g. new railways coming to the area), demographics, political features that may tend to affect the price of properties; buyer data relating to personal circumstances of potential buyers of the property 5 include factors that are important to the buyer and may affect the price through the buyer’s eyes. They may include data such as the age of the prospective buyer, age of family members, geographic location of the workplace relative to the property, location of family and friends, and other factors.

In an embodiment, all of socio-economic data, macro-economic trend data and buyer 0 data may be utilised and processed to establish an estimated selling value for the property.

In an embodiment, physical attribute data on the physical attributes of the property are also input to be processed to establish a value, together with the other data types.

In an embodiment, the method is implemented for a plurality of properties, to provide property valuations for a plurality of properties. This may be done for many geographic areas 2 5 and for many properties.

In an embodiment, the method comprises the further step of controlling an interface to generate information on the estimated selling values which may be accessed and viewed by users.

In an embodiment, a plurality of data sources are continually accessed and data 3 0 obtained from them. Selling values are updated as the data changes. The interface therefore -4- 2016250337 24 Oct 2016 advantageously gives a “dynamic” view for users of property price changes in any particular geographical area.

In an embodiment, as well as providing an estimated selling value, the method comprises the step of obtaining data values for buyer data relating to personal circumstances 5 of a potential buyer of the property, and providing an estimated buyer value for the property.

In an embodiment, the method comprises the further step of determining whether the selling value and buyer value coincide, and alerting a buyer and/or seller if they coincide. Advantageously, a buyer and seller can be matched so that a property transaction can be facilitated. 0 In accordance with a second aspect, the present invention provides a method of facilitating the evaluation and sale of real estate comprising the steps of obtaining physical attribute data on the physical attributes of properties; accessing one or more data sources and continually obtaining data values of other data which may affect property prices; processing the obtained other data and the physical attribute data to establish values for the properties; 5 controlling an interface to generate information on the estimated values, the interface being accessible by users, and updating the information on the estimated values as the values change. In accordance with a third aspect, the present invention provides an apparatus for facilitating the valuation and sale of real estate, comprising a computing device having a processor, a memory and an operating system supporting computer processes; a data 0 acquisition process, arranged to access one or more data sources and obtain data values for one or more of the following data types: socio-economic data relating to socio-economic factors in a geographical area where a property is located; macro-economic trend data relating to one or more of infrastructure, demographics and political features in a geographical area where the property is located; buyer data relating to personal circumstances 2 5 of potential buyers of the property; and a valuation process, arranged to process the data values by applying relative weightings to the data values, and processing the weighted data to establish an estimated selling value for the property.

In accordance with a fourth aspect, the present invention provides a computer program, comprising instructions for controlling a computer to implement a method in 3 0 accordance with the first aspect or second aspect of the invention. -5- 2016250337 24 Oct 2016

In accordance with a fifth aspect, the present invention provides a computer readable medium, providing a computer program in accordance with the fourth aspect of the invention.

In accordance with a sixth aspect, the present invention provides a data signal, comprising a computer program in accordance with the fourth aspect of the invention. 5

Brief Description of the Drawings

Embodiments will now be described by way of example only, with reference to the accompanying drawings in which

Fig. 1 illustrates an apparatus in accordance with an embodiment of the present 0 invention arranged to facilitate a valuation of real estate, and to facilitate operation of transactions between buyers and sellers;

Fig. 2 is an example block diagram of a computing system which may be utilised in implementation of an embodiment of the present invention;

Fig. 3 shows a flow diagram of a method of facilitating the evaluation and sale of real 5 estate in accordance with an embodiment of the present invention;

Fig. 4 is an illustration of information which may be utilised in this embodiment to obtain a valuation of property;

Fig. 5 is an illustration of a map showing socio-economic data in the form of “detractors” and “attractors”, and also showing previous property transactions; 2 0 Fig. 6 is a plot of effect of socio-economic data in relation to the distance from the subject property;

Fig. 7 shows a previous property transaction, weighted in dependence on an index that takes into account the “social mood” in the location;

Fig. 8 shows an example of a GA/NN network, which may be utilised in accordance 2 5 with an embodiment of the present invention;

Fig. 9 shows the change in demographics of South Australia between 2001 and 2006; -6- 2016250337 24 Oct 2016

Fig. 10 shows the relative change in spending of an average person between the ages of 20 and 90;

Fig. 11 shows a proposed extension to a railway in Sydney;

Figs. 12 shows a map of the political electorates of Sydney; 5 Fig. 13 shows an example of a Monte Carlo Simulation;

Fig. 14 shows a matching of buyer and seller property evaluations; and

Fig. 15 shows a system / platform for processing the method of Fig. 3 and establishing the matching of buyer and seller property evaluations of Fig. 14. 0

Detailed Description

In the following detailed description, reference is made to accompanying drawings which form a part of the detailed description. The illustrative embodiments described in the 5 detailed description, depicted in the drawings and defined in the claims, are not intended to be limiting. Other embodiments may be utilised and other changes may be made without departing from the spirit or scope of the subject matter presented. It will be readily understood that the aspects of the present disclosure, as generally described herein and illustrated in the drawings can be arranged, substituted, combined, separated and designed in 2 0 a wide variety of different configurations, all of which are contemplated in this disclosure.

Fig. 1 illustrates an apparatus which, in this embodiment, is arranged to facilitate an accurate valuation of real estate, and to facilitate operation of transactions between buyers and sellers.

The apparatus is generally represented by reference numeral 100. It comprises a host 25 computing system 102, shown in this example as comprising server computers 104 which supports computer processes 105 and a big data engine 107. A database 120 is implemented by the computing system 104. A communications interface 125 is implemented to communicate with other network nodes, via a network (such as the Internet, for example, but not limited thereto). -7- 2016250337 24 Oct 2016

The computer processes 105 include a data acquisition process which is arranged to obtain data from a plurality of data sources 106, via the communications 125. The data sources, in this example, may include such sources as social networks, real estate websites, government and council sources, news sources and more data sources. The data acquisition 5 process is arranged to obtain data values from these data sources 106.

The computer processes 105 also include a valuation process, which is arranged to process the data values to establish an estimated selling value for the property. The valuation process utilises the big data engine 107 and various algorithms to process large amounts of data obtained from the data sources 106, to produce, advantageously, an accurate value for a 0 real estate property.

In this embodiment, customer devices 110 may have access to the system to view the outcomes of the process. In this example, the system 102 can “match” buyers with sellers via the devices 110 and the system 102.

The customer devices may be any device which can be utilised, including a mobile 5 device, such as a smartphone, laptop, tablet device, or a PC or any other computing or communication device.

In this example, real estate agents 114 can also engage with the system 102 to facilitate transactions of real estate. The real estate devices 114 may be of similar type to the customer devices. _ 0 Service provider devices 112 may be operated by service providers such as financial professionals, tradesmen, etc., so that they can interact with the system 102 to facilitate property transactions and transfer, and the logistics to facilitate this.

Figure 2 is a schematic block diagram of a computing system which may be used to implement the apparatus 102. A similar computing system may be used to implement devices 25 110, 112 and 114.

The illustrated computing system comprises a computer 201 which includes a processor 202 and memory 203. The processor 202 is arranged to process programme instructions and data in a known manner. Memory 203 is arranged to store programme instructions and data also in a known manner. Processor 202 may constitute one or more 3 0 processing means, such as integrated circuit processors. The memory 203 may comprise any -8- 2016250337 24 Oct 2016 known memory architecture and may include hard disk, IC memory (ROM, PROM, RAM, etc.), floppy disks and other types of additional memory such as CD ROM, and any other type of memory. A BUS 204 is provided for communication between the processor 202 and memory 5 203 and also communication with external components. In this case the external components include a user interface 205. The user interface 205 includes a visual display unit 206 for displaying information to a user. The VDU 206 may display information in graphical format or any other format depending upon the programme instructions being processed by processor 202. 0 The user interface 205 also includes user input means 207 which in this example include a keyboard 208 (which in this example may be a standard QWERTY keyboard) and a mouse 209. The mouse 209 may be used to manipulate a graphical user interface (GUI) if a GUI is provided by software running on the computer. A network connection 210 is also provided for connecting to a network which may include a communication network 210 and 5 other computers/computing systems.

The computing system of Figure 2 may be implemented by any known type of computing hardware such as, for example, a PC, by a number of networked PCs if required to implement a system of this embodiment, by a “mainframe architecture” including a remote computer and user workstations connected to the remote computer, by a client-server 0 architecture, including a client computer accessing a server computer over a network, or by any other computing architecture.

Parts of the system or the entirety of the system may be housed in the “cloud”.

This embodiment of the present invention is implemented by appropriate software providing instructions for operation of the computing system hardware to implement the 2 5 apparatus of the embodiment and implement the method of the embodiment

Part of the system or the entire computer system may be portable, and may be implemented, for example, by a laptop or tablet computer, smartphone or other portable device.

The computing system is provided with an operating system and various computer 3 0 processes to implement functionality. The computer processes may be implemented as -9- 2016250337 24 Oct 2016 separate modules, which may share common foundations such as routines and sub-routines. The computer processes may be implemented in any suitable way and are not limited to separate modules. Any software/hardware architecture that implements the functionality may be utilised. The computing system may be implemented as a server computing system, or 5 utilising computer resources in the cloud, or any other computer resources. In this embodiment, the host system (e.g. 102 in Fig. 1) is implemented utilising cloud resources. A method of facilitating the evaluation and sale of real estate in accordance with an embodiment, will now be described in further detail. The method is implemented by the host system 102 and the computer processes 105. An overview of the method is shown in Fig. 3. 0 In one form, the method may include the step of obtaining physical attribute data 3 on the physical attributes of a property. The physical attribute data can include property specific data such as the property type (e.g. house, apartment, commercial, property age, number of rooms, number of bedrooms, number of bathrooms, heating/cooling systems, land size, building size, number of car parks and outdoor space). The step of obtaining physical attribute data may 5 comprise the step of obtaining physical attributes (e.g. sale price) of a plurality of properties from a data source 1 (e.g. a dynamic table of previous sales on the internet). In one form, the physical attributes data may also include data on the running costs of a property (e.g. a seller may upload electricity, gas and water bills), data on the appliances (e.g. data on the technology type, age and condition to enable an estimate of the value or depreciation of the 0 appliances), data on the sustainability features (e.g. solar panels, water tanks etc.), data on the condition of the property (e.g. data on the condition of the wiring, roof age / condition etc.). The physical attribute data can be weighted in dependence on the property, buyer and location. To increase the transparency of the system, sellers are able to be rated by users of the system to ensure that uploaded physical attribute data is accurate, or by enabling service 2 5 provider to provide third party observations. Further, a location algorithm allows calculation of a ‘base’ value per square foot area and room count of a property. This algorithm allows the seller to supply information to demonstrate that the actual value of the property is above that ‘base’.

The method includes the step of accessing a plurality of data sources 1 (via the data 3 0 acquisition process) and obtaining data values for one or more of socio-economic data 5, macro-economic trend data 7 and buyer data 9, before processing (via the valuation process and big data engine 107) 11 the data values and physical attribute data to establish an -10- 2016250337 24 Oct 2016 estimated selling value for the property. Each of these data types, along with data sources for obtaining the data types, will now be described in detail.

Socio-economic data 5 relates to socio-economic factors in a geographical area where the property is located. Socio-economic data will be described with reference to Figs. 5-8. 5 Fig. 5 shows a map 13 that includes a subject property 15 and previous transactions 15,17,19,21,23,25. Previous transactions are in the form of previous property sales located within a set distance of the subject property 15. Each of the previous transactions may include a subset of data further detailing the transaction (e.g. sale date, size of property, age of previous buyer / seller). 0 The map 13 also includes attractors 27,29. The attractors are in the form of purchase attractions within a set distance of the property (e.g. hospitals, schools, public transport, civic amenities, shops, entertainment, good neighbour, mood of location, quality of life index). Each of the attractors may include a subset of data further detailing the attractor (e.g. attraction type, appearance date, longevity factor). The map 13 also includes detractors 31. 5 Detractors are in the form of purchase detractions within a set distance of the property (e.g. prisons, poorly maintained properties, noise, emissions, odour, line of sight, infestations, crime, bad neighbour, social events, etc.). Each of the detractions may include a subset of data further detailing the detractor (e.g. attraction type, appearance date, persistence factor). As will be evident to the skilled addressee, the allocation of ‘attractor’ or ‘detractor’ to 0 certain socio-economic data will depend on the perspective of the buyer (e.g. a local bar may be attractive to some buyers, and unattractive to others). The allocation of ‘attractor’ or ‘detractor’ to certain socio-economic data can be established by comparing the socioeconomic data with buyer data.

As is shown in Fig. 6, the effect of socio-economic data can vary in dependence on 2 5 the distance from the subject property 13. For example, to some buyers, having a bar located next door to the subject property 13 may be considered a detraction. However, having the same bar located 200 metres from the subject property 13 may be a significant attraction. This non-linear relationship 33 is shown in Fig. 6. Other attractors may have an exponential relationship 35 on the evaluation, whereby the influence of the attractor is strong if located 30 close to the subject property and becomes far less influential with increasing distance from the subject property. Detractors may have a similar influence on the property evaluation. -11 - 2016250337 24 Oct 2016

Therefore, the effect of socio-economic data can therefore be weighted in dependence on data type and buyer data.

Previous transactions can also be weighted in dependence of an index that may take into account the social mood in the location (e.g. immediate location or broad location) of the 5 subject property 13, the quality of life in the location of the subject property 13 and the ‘prestige’ of owning a property in the location of the subject property 13. The effect of each of these indexes can be combined into a single index that provides a weighting of a previous transaction over time (e.g. see Fig. 7). A genetic algorithm / neutral network (GA/NN) can also be applied to the socio-0 economic data. An example of a GA/NN network is shown in Fig. 8. With potentially hundreds of thousands of data points and dozens of factors that will affect the property evaluation, deploying a GA/NN ‘training’ system can assist to work out the optimum weighting/s) of each of the factors. A socio-economic value may be established by an algorithm that determines the 5 weighted socio-economic factors (e.g. a summation of the previous transaction price achieved [weighted to take into account: distance from property, floor area, room types, location attractiveness since transaction), social mood since transaction, quality of life index since transaction], attractors [weighted to take into account: distance from property, since previous transactions] and detractors [weighted to take into account: distance from property, 0 since previous transactions]).

Macro-economic trend data relates to one or more of infrastructure, demographics and political features in a geographical area where the property is located. Examples of macro-economic trend data are shown in Figs. 9 to 12. A pertinent issue for an individual property valuation is whether a future change has a specific local effect. Macro-economic trend data 2 5 can be split into three primary categories, demographics (Figs. 9 & 10), impending infrastructure changes (Fig. 11) and politics (Fig. 12).

Fig. 9 shows the change in demographics of South Australian between 2001 and 2006. By taking into account demographic data as birth rate, immigration rate and pension rate, the change in value of socio-economic and property data can be predicted. For example, 3 0 the weighting of school proximity may increase in attraction (i.e. be a more heavily weighted attractor) with rising birth rate, access to hospital may increase in attraction (i.e. be a more - 12- 2016250337 24 Oct 2016 heavily weighted attractor) with an increase in older residents and the relative importance of bedrooms/bathrooms may be affected by birth rate.

Fig. 10 shows the relative change in spending of an average person between the ages of 20 and 90. An algorithm can account for the average age of population in in a particular 5 location (e.g. within the suburb of the subject property or within the city of the subject property) to establish whether the local economy will grow or shrink relative to the economy at large (e.g. the state or country). Average earnings can alter the relative value of different property types. Marriage/divorce rate can alter relative value of different property types. For example, the relative value of single occupancy property may increase or decrease in 0 dependence on the divorce rate in a particular location. This can be established and weighted accordingly in dependence on the buyer type.

Fig. 11 shows a proposed extension to a railway (e.g. an example of an impending infrastructure change). Proposed infrastructure can alter the attractors and detractors in a particular location. This relationship may vary over time. For example, during construction 5 the site can be a value detractor. However, once completed, the infrastructure can be an attractor (e.g. a new station, a new hospital, a new development with outdoor landscaping, a new university or school, a new road). A software application is able to search data sources (e.g. via a network) and establish the impending infrastructure projects. In dependence on the type of infrastructure, the relative requirement in the location for the infrastructure and the 0 stage of construction of the infrastructure, the macro-economic trend data factor is established and weighted accordingly, before being combined with the other macro-economic trend data factors.

Figs. 12 shows a map of the political electorates of Sydney. The algorithm is able to establish changing political views that may make particular locations more or less attractive 2 5 than others. The software application is able to search data sources (e.g. the internet) and the algorithm is able to establish the political changes for a subject location in dependence on the searched data sources. The algorithm can connect political shifts to attractors, detractors, likelihood of new attractors, likelihood that services will increase/decrease, employment, likely crime rate and indices of multiple deprivations. Each factor can be weighted 3 0 accordingly, before being combined with the other macro-economic trend data factors.

Fig. 13 shows an example of the Monte Carlo Simulation. Given the inherent uncertainties regarding prediction of future events (especially whether an infrastructure - 13- 2016250337 24 Oct 2016 project goes ahead or not, or changes to political views), in at least one embodiment the user (e.g. the buyer or seller) can be given the option to run a Monte Carlo simulation model to obtain ‘best/’worst’/’most likely’ value predictions, and also to input their own opinion about whether an event will happen or not. 5 To obtain the data values for socio-economic data 5, macro-economic trend data 7 and buyer data 9, the method may be able to access a plurality of data sources (e.g. a plurality of data sources available over a network and via the internet). This step may be dynamic, such that the data values are able to be continually obtained from the data sources. In dependence on the continually varying data values, the step of the processing can include the step of 0 updating the estimated selling value as data values change.

An interface, in the form of a website publically accessible on the internet, can be used to generate information on the estimated selling values. The interface is accessible by users and is able to display and continually update the information on the estimated selling values as the estimated selling values change in dependence on the continually varying input 5 data values. As such, the method is able to provide a dynamic “index” of property prices in a particular geographical region (e.g.the geographical region where the subject property is located ).

The interface which the users can access is implemented by an interface process of the computer processes 105. It may be a web based interface accessible by devices 110, 114, 112 0 over the communications 125.

In one form, the method includes the step of obtaining data on property alterations (e.g. alterations to the property performed by the seller). The quality of alterations performed by the seller can impact the established property evaluation. It can be a challenge for a buyer to determine the quality of alterations (e.g. renovations) performed by the seller. For 2 5 example, if an alteration was performed professionally, this may increase the established property evaluation. If not, the quality of the alteration may detract from established property evaluation. In one form, sellers are able to access the interface to upload invoices/certificates from approved builders. In one form, prospective buyers are able to access the interface to upload opinions on the quality or functionality of the alteration. This input data is then 3 0 processed and combined with other weighted data values to establish the property evaluation. - 14- 2016250337 24 Oct 2016

Commonly, buyers start a property search on-line and review photos or video walkthroughs. A useful resource for buyers and sellers is to gather feedback from prospective buyers as they make their virtual tour. In one form, users are able to establish quantitative values by accessing the interface and rating the decorative order of a particular space within 5 the subject property on a scale (e.g. good, average, bad or numerical - 1-9). In one form, users are able to establish qualitative (e.g. PanSensic) data by accessing the interface and inputting a description of impressions of a particular space within the subject property. For example, a prospective buyer may be able to input data on what they would change. This can be advantageous for a seller, as the seller can receive useful information from prospective 0 buyers, and for a buyer who may be provided with outcomes of other buyer perspectives. The cculmination of the various feedback scores can provide another a factor that either increases or decreases the property evaluation relative to a ‘base’ evaluation.

In one form, the method includes the step of automatically analysing input data (e.g. photos of a subject property) and processing the analysed input data to establish property 5 evaluation. For example, the colour of walls, carpet and tiles can be established from input photos of the subject property and then then be compared with colour trend data. Colour trend data can be established from data sources available on the internet (e.g. from a social media analysis) in order to generate a value modification factor (e.g. lots of ‘out of date’ wall colours will detract from value, whereas wall colours that are on-trend can increase the value 0 of the property). In another form, photo/video analysis is used to make an assessment of the claustrophobic (e.g. openness and access to natural light) effect in property or openness. This factor can be given a weighting to add to the overall weighting factor when establishing the property evaluation.

As noted above, the method can include the step of obtaining buyer data, before 2 5 processing the data to establish an estimated selling value for the property. This step can be performed in at least two ways. In one form, the buyer is able to access the interface to input data relating to personal circumstances (e.g. age, location of family and friends, marriage status, hobbies, location of workplace, desire to add value to property, intention to use property as home or investment, salary etc.). This input buyer data is able to be processed to 3 0 increase or decrease the processed property evaluation for that particular prospective buyer.

For example, a subject property that is located close to the family and friends of a prospective buyer may be worth more to a that particular buyer than to another buyer who’s family lives a long distance from the subject property. Therefore, following the processing of the buyer - 15- 2016250337 24 Oct 2016 data, the property value displayed on the user interface would change in dependence on the buyer (i.e. the property value may be $50,000 higher for the prospective buyer whose family lives close to the subject property). In one form, the buyer is able to run scenarios to explore the significance of possible events such as life changes (e.g. children, marriage, etc.), 5 economic changes and political changes. In one form, a user is able to input or obtain information in relation to a likely expense (e.g. the requirement to spend money on a property to accommodate a life change, or because something expensive, such as a roof, needs to be replaced). These factors are able to be processed to increase or decrease the processed property evaluation for that particular prospective buyer. 0 In another form, the buyer data is able to be established automatically. In this form, the step of accessing the plurality of data sources comprises obtaining data values for buyer data relating to personal circumstances of a potential buyer of the property. For example, data sources such as social media are able to be accessed to establish the likely prospective buyer for particular locations. In dependence on the location of the property, the likely buyer can 5 automatically be set such that the buyer does not need to input personal information into the user interface. In another form, the method is able to access personal information of the prospective buyer using the interface to establish the buyer data. Previous websites accessed by the prospective buyer (e.g. previous searches for property, previous searches in relation to particular interests) can provide information about the prospective buyer such that the 0 prospective buyer does not need to input personal information into the user interface. For example, a user that commonly accesses websites that provide information on ocean conditions may value a property located near the ocean higher than a person who commonly searches websites that relate to online shopping. The buyer data may comprise psychometric data relating to the buyer. 25 In one form, buyer data may be established based on demographics, and a general knowledge of the age, gender, personal circumstances of a buyer. Big data may be used to establish requirements for different “types” of buyer.

The buyer data is used by the processing to determine weightings to be applied in the processing to affect the outcome price. The buyer data can include specific buyer data (for a 3 0 particular buyer) and generic buyer data (a “type” of buyer e.g. based on demographics or other data). - 16- 2016250337 24 Oct 2016

In one form, the method is able to determine whether the selling property value and buyer property value coincide, utilising a matching process of the computer processes 105.

In dependence on this determination, the method is then able to alerting a buyer 100 and/or seller 200 if the buyer value and selling value coincide. An example of this 5 coincidence of buyer and seller property values is shown in Figs. 14 and 15. An algorithm is able to establish a property value range 41 for the seller in dependence on the weighted data values for socio-economic data (e.g. location data) 5, macro-economic trend data (e.g. expected future macro-economic climate data) 7, physical attribute data 3 (e.g. property data) and alteration data 37 (e.g. seller alteration data). The algorithm is able to also establish a 0 property value range 43 for the buyer in dependence on the weighted data value for the buyer data 39 (e.g. data that details what a property is worth to an actual buyer or particular buyer type). The algorithm can then compare the seller range 41 to the buyer range 43 to establish either or both of a range 45 of possible agreement or a suggested fair property evaluation 47 for both the seller and the buyer. As will be evident to the skilled addressee, the established 5 range 45 of possible agreed property evaluation and the suggested fair price 47 has numerous applications. In one form, sellers and buyers can be matched in a matching process. For example, a prospective buyer may be presented (e.g. on a monitor of the interface) with a list of properties where there is an overlap between the seller range 47 and the buyer range 49.

The buyer may then select from the displayed properties to send a notification to a seller. 0 With very little communication, the buyer and seller can then agree on a price within the overlapping range 47. In another form, for example, a prospective buyer may be presented (e.g. on a monitor of the interface) with a list of properties, each of which includes the suggested fair price 47 for each property. The user can the select a property and agree to the suggested fair price 47. A notification can then be sent to the seller to accept of decline the 2 5 buyers offer of the suggested fair price 47. Therefore, with no communication, the buyer and seller can then agree on a transaction of the property. In this way, the method can provide for a platform that is able to match buyers and sellers without the need for an intermediary person (e.g. a real estate agent). In other words, the disclosed method includes the step of facilitating a network connection between the buyer and seller and generating an interface 3 0 which enables implementation of a transaction for sale of a property. Advantageously, this saves on the costs associated with real estate agents and is able to provide a positive emotional buying and selling experience for property transactions. In another form, a similar process can be applied for rental evaluations and matching of renters and landlords. 2016250337 24 Oct 2016 - 17-

In one form, the disclosed method includes the step of enabling agent access to the interface, to enable agent facilitation of the sale. As such, an agent, such as a real estate agent, is able to access the real estate platform. This can either provide an alternate selling source for traditional real estate agents, or provide for an online real estate agency (e.g. a ‘thin’ 5 agency that requires less people, minimal office space etc.). Buyers and sellers can choose to use an agent via the platform, to facilitate the process.

To further streamline the real estate buying and selling process, the disclosed method may include the step of enabling network connections to service providers to enable service providers 63 (e.g. financial service providers, legal service providers, removalists, etc.) to be 0 engaged to facilitate property transaction and transfer. Access to the system may be had by the service provider devices 112 (Figure 1). Users may “post” service provider requirements, or select particular service providers published on the platform.

Embodiments of the invention may be used with any type of real estate property. This can be domestic, personal property, commercial property, government housing, or any other 5 type of property.

The valuation established by embodiments of the present invention can be used for other purposes than just buying and selling property. It could be used to verify a value for security for a loan, for example, such as a mortgage or another loan. It could be used for other applications. 0 In the claims which follow and in the preceding summary except where the context requires otherwise due to express language or necessary implication, the word “comprising” is used in the sense of “including”, that is, the features as above may be associated with further features in various embodiments.

Variations and modifications may be made to the parts previously described without 2 5 departing from the spirit or ambit of the disclosure.

Claims (30)

1. Claims

   1. A method of facilitating the evaluation and sale of real estate, comprising the steps of: accessing one or more data sources and obtaining data values for one or more of the following data types: socio-economic data relating to socio-economic factors in a geographical area where a property is located; macro-economic trend data relating to one or more of infrastructure, demographics and political features in a geographical area where the property is located; buyer data relating to personal circumstances of potential buyers of the property; processing the data values, the processing comprising applying relative weightings to the data values, and processing the weighted data to establish an estimated selling value for the property.
2. 2. A method in accordance with Claim 1, comprising the further step of obtaining physical attribute data on the physical attributes of the property, and the step of processing comprises the step of processing the physical attribute data together with the data values, and comprises applying relative weightings to the physical attribute data and the data values.
3. 3. A method in accordance with Claim 1 or Claim 2, wherein the step processing comprises the step of providing estimated selling values for a plurality of properties.
4. 4. A method in accordance with Claim 1, 2 or 3, wherein the step of accessing a plurality of data sources, comprises continually obtaining data from the data sources, and the processing comprises the step of updating the estimated selling value as data values change.
5. 5. A method in accordance with Claim 4, comprising the further step of controlling an interface to generate information on the estimated selling values, the interface being accessible by users, and to update the information on the estimated selling values as the estimated selling values change.
6. 6. A method in accordance with any one of the preceding Claims, wherein the step of accessing the plurality of data sources comprises obtaining data values for buyer data relating to personal circumstances of a potential buyer of the property, and the step of processing comprises the step of providing an estimated buyer value for the property.
7. 7. A method in accordance with Claim 6, comprising the further step of determining whether the selling property value and buyer property value coincide, and alerting a buyer and/or seller if the buyer value and selling value coincide.
8. 8. A method in accordance with Claim 7, wherein the establishing of an estimated selling value comprises the step of providing a selling value range, and the step of providing a buyer value comprises providing a buyer value range, and the step of determining whether the buyer and selling value coincide comprises determining whether the ranges overlap.
9. 9. A method in accordance with any one of the preceding claims, wherein the buyer data comprises psychometric data relating to the buyer.
10. 10. A method in accordance with any one of the preceding claims, wherein the one or more data sources accessed to obtain the data values comprises a social media data source.
11. 11. A method in accordance with any one of the preceding claims, wherein the socioeconomic data includes at least one of social mood data, quality of life data, attractor data and detractor data.
12. 12. A method in accordance with claim 11, wherein the attractor data is in the form of data relating to a purchase attraction that is located within a pre-determined distance of the property.
13. 13. A method in accordance with claim 12, wherein the detractor data is in the form of data relating to a purchase detraction that is located within a pre-determined distance of the property.
14. 14. A method in accordance with claim 12 or 13, further comprising the steps of; determining an attractor distance for each purchase attraction, the attractor distance representing a physical distance between the property and respective purchase attractions; and determining a detractor distance for each purchase detraction, the detractor distance representing a physical distance between the property and respective purchase detractions.
15. 15. A method in accordance with claim 14, wherein the attractor data is weighted in dependence on the determined attractor distance, and the detractor data is weighted in dependence on the determined detractor distance.
16. 16. A method in accordance with any one of claims 11 to 15, further comprising determining a delta factor in dependence on the social mood data, the delta factor representing a difference in the social mood data over a period of time between a date of a previous sale of a property and a present date, wherein the established estimated selling value for the property is weighted in dependence on the determined delta factor.
17. 17. A method in accordance with any one the preceding claims, further comprising optimising the applied relative weightings to the data values using a genetic algorithm and/or neutral network.
18. 18. A method in accordance with any one the preceding claims, further comprising obtaining property specific data and processing the obtained property specific data to establish the estimated selling value for the property.
19. 19. A method in accordance with claim 18, wherein the property specific data includes at least one image of the property.
20. 20. A method of facilitating the evaluation and sale of real estate, comprising the steps of: obtaining physical attribute data on the physical attributes of properties; accessing one or more data sources and continually obtaining data values of other data which may affect property prices; processing the obtained other data and the physical attribute data to establish values for the properties; controlling an interface to generate information on the estimated values, the interface being accessible by users, and updating the information on the estimated values as the values change.
21. 21. An apparatus for facilitating the valuation and sale of real estate, comprising a computing device having a processor, a memory and an operating system supporting computer processes; a data acquisition process, arranged to access one or more data sources and obtain data values for one or more of the following data types: socio-economic data relating to socio-economic factors in a geographical area where a property is located; macro-economic trend data relating to one or more of infrastructure, demographics and political features in a geographical area where the property is located; buyer data relating to personal circumstances of potential buyers of the property; and a valuation process, arranged to process the data values by applying relative weightings to the data values, and processing the weighted data to establish an estimated selling value for the property.
22. 22. An apparatus in accordance with Claim 21, wherein the data acquisition process is also arranged to obtain physical attribute data on physical attributes of the property, and the valuation process is arranged to process the physical attribute data together with the data values, by applying relative weightings to the physical attribute data and the data values.
23. 23. An apparatus in accordance with Claim 21 or Claim 22, wherein the data acquisition process is arranged to continually obtain data from the data sources, and the valuation process is arranged to update the estimated selling value as data values change.
24. 24. An apparatus in accordance with Claim 21, 22 or 23, comprising an interface process arranged to generate estimated selling value information for a user interface, and to update the information on the estimated selling values as the estimated selling values change.
25. 25. An apparatus in accordance with any one of Claims 21 to 24, wherein the data acquisition process is arranged to obtain data values for buyer data relating to personal circumstances of a potential buyer of the property, and the valuation process is arranged to provide an estimated buyer value for the property.
26. 26. An apparatus in accordance with any one of Claims 21 to 25, further comprising a matching process, arranged to determine whether the selling property value and buyer property value coincide, and the interface process is arranged to alert a buyer and/or seller if the buyer value and selling value coincide.
27. 27. An apparatus wherein the valuation process is arranged to provide an estimated selling value range and an estimated buyer value range, and the matching process is arranged to determine that the buyer and selling value coincide when the ranges overlap.
28. 28. A computer program, comprising instructions for controlling a computer to implement a method in accordance with any one of Claims 1 to 20.
29. 29. A computer readable medium, providing a computer program in accordance with Claim 28.
30. 30. A data signal, comprising a computer program in accordance with Claim 28.