Introduction
Assessing the market value of real property has many applications such as conducting market performance audits, providing the general public with access to information, and management of government regulatory functions related to tax and legal systems. Mass appraisal is the process of valuing a group of properties at a given date using common data, standardised methods, and statistical testing. It is vital that the data used for mass appraisal is accurate and consistent, otherwise, there can be major repercussions for the overall valuation process. The system must also be restricted to relevant land, improvement, and location features data. Conducting mass appraisals requires a large number of human resources and finances. However, with the introduction of Big Data technologies and artificial intelligence, data can now be incorporated into digitised databases and can be analysed at a much greater speed and at a far lesser cost.
Graana.com in collaboration with Iqbal Institute of Policy Studies (IIPS) brings forward the concept of ‘Mass Appraisal of Real Property’ and highlights how mass appraisals are conducted and how digital technologies are bringing innovation in the practice.
Mass Appraisals and Collecting and Maintaining Property Data
A property appraisal is an unbiased opinion of value used for real estate finance transactions. Appraisals are required by the government for multiple reasons and include information like the type of property inspection, approaches to the value required, and lender specific requirements. Mass appraisals are a specific tool used to value large numbers of properties for taxation and land management (Griffith, 2015). The process differs from appraisals that are conducted for single properties as those methods cannot be scaled to give value for multiple properties without complications. Local governments used to provide local inspectors with cost value cards that were used to value property. However, this cost system can have deficiencies as it does not account for local differences and market conditions. With the introduction of computer systems that used software and large data sets to analyse sales information, property characteristics, and statistical techniques, computer-aided mass appraisal (CAMA) systems are being employed in many parts of the world.
Property valuations have many applications in the regulation and market function of an economy. They are used in the calculation of tax, buying and selling of property, investment design, and risk assessment of future development projects. Providing a uniform and accurate property valuation requires complete and up-to-date property data. The easiest way to collect this data authentically is to deploy geographical information systems (GIS) that use digital mapping to fuse multiple layers of data with one image of a virtual map. Information like property ownership, location, size, use, physical characteristics, sales price, rent, costs, and operating expenses can be easily incorporated into a graphical image of a map, and when linked to a mass appraisal system, the results can be very useful. Data on property characteristics must also be gathered and maintained regularly by updating building permits, conducting aerial photography, gathering information derived from third party sources, and periodic field inspections. Data collection must also encompass sales data, income and expense data, and cost and depreciation data (IAAO, 2017). All in all, mass appraisals now require large amounts of data that is secure, reliable, and transparent.
How Technology is Changing Mass Appraisal?
When it comes to processing large amounts of data, computers are known to perform faster and more accurately than human beings. Technologies like Big Data analytics and artificial intelligence are gaining popularity among the masses, but few understand how the technology can help in mass appraisal systems (Dimopoulos & Moulas, 2016). Computers can store vast amounts of data in relational databases that can be linked together to access multiple levels of data related to a property in just a few seconds. Inconsistencies in the data can be identified and fixed before performing the analysis, reducing the error rate of the appraisal. Once the data is prepared, an analysis based on statistical tools can be carried out within a few minutes and results can be visualised in the form of charts and graphs for better insights into the market. Artificial intelligence systems learn trends and patterns in the data, and once the system analyses one set of data, it can be used to train multiple different models for all other kinds of analysis. Therefore, the application of Big Data analytics and artificial intelligence in mass appraisal systems can significantly reduce the cost of performing such appraisal, while delivering a massive increase in efficiency and accuracy (Yilmazer, 2020).
Computer-Aided Mass Appraisal (CAMA) Systems
The process of computer-aided mass appraisal systems involves three steps namely, data collection, modelling, and application. The process starts by gathering data on sold and unsold properties and building an accurate inventory of planned and unplanned areas. Different models based on statistical techniques are used to develop an appraisal algorithm that replicates the market when assigning value to the various features of a property. These techniques can include linear or multiple regression statistical analytics, trend analysis, and other statistical inferences. The model must then be tested multiple times to train the algorithms for accuracy. This is achieved by comparing the sale price to the value assigned to the property by the system. Once the CAMA model is developed and finalised, it is then applied to a wide range of properties to ensure that all valuations are carried out equitably across the system. Government and regulatory bodies can use standards that check for inconsistencies and bias in the valuation model and ensure that a transparent set of rules is applied across the board while training such systems. Once these regulations have been placed, there should be no hesitation to use the valuations in regulatory functions such as tax, and other market purposes (Harris Govern, 2021).
Conclusion
Mass appraisals include valuing a group of properties at a given date using common data, standardised methods, and statistical testing. The practice is performed to fulfil functions like performance audits, providing access to public information, and management of government and regulatory functions related to tax and legal systems. However, regular mass appraisals require a large number of human resources and financial reserves. With the introduction of Big data technologies and artificial intelligence, data can now be incorporated into digitised databases and can be analysed at a much greater speed and accuracy at a far lesser cost.
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