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Real estate valuation

Real estate valuation

How can machine learning help in real estate valuation?

Real estate prices are only measured objectively when properties change hands. This means that prices are infrequently observed for the same asset. In between transactions, property valuations are used— the most likely price to be obtained in the market, had the property been put up for sale. It’s a hypothetical value, not the actual registered price1. This value is used in numerous instances: by real estate professionals, by bankers (which mortgage properties), by insurance brokers, by tax attorneys, by property owners (who rent their property), by married property owners (who get a divorce with one spouse wanting to ‘rebuy’ the share of the other), by notary and lawyers who manage an estate.

This points to the likely modelling nature of real estate valuation. The longer a property has been owned, the further from a previous transaction we stand, the murkier the estimates and thus less reliable the valuation.

Problem to solve

  • How can the valuation of a house or an apartment be predicted?
  • Can this price estimation be made quickly?
  • How objective is this estimation?
  • Benefits of TADA
    in the Real Estate Sector

    Finding the market value of a property is an essential starting point in any estate or real estate estimation. Real estate professionals, bankers, property owners, insurance brokers, renters, estate attorneys can use predictive models to get fair valuations.
    However, they are not data scientists and may not have the skills in machine learning nor in software coding to build predictive models. Moreover, they have usually accumulated data about previous similar transactions which are in the range of dozens, sometimes hundreds, hardly thousands. These data include: property age, previous selling price, date of previous transaction, distance to the closest metro station, number of shops in the vicinity, quality of the school district, size. These are typically Small Data. Standard machine learning tools work well with Big Data but do not perform as well with Small Data.

    By using an automated machine learning solution such as TADA, real estate professionals, bankers, property owners, insurance brokers, renters, estate attorneys can now make a quick and precise valuation of their property.

    MyDataModels allows these professionals to build predictive models from Small Data without any kind of training. They can use their collected data directly. No normalisation, nor outlier’s management, nor feature engineering is required. This simple data preparation coupled with our algorithms have provided us with results obtained with a few clicks in less than a minute on a regular laptop.

    This estimate of valuation is meant to be a starting point for a conversation about value. That conversation, ultimately, needs to involve other means of value, include real estate professionals like an agent or broker, or an appraiser; people who have expert insight into local areas and have seen the inside of a home and can compare it to other comparable homes.

    TADA brings new possibilities for pricing valuation

    Pricing is key in real estate. It impacts the selling/buying price of course, but also the property taxes, the insurance, the estates. Hence, it is essential for all the people involved to have a fair and objective starting point for discussing valuation.

    “Predictive model allows extremely accurate predictions”

    By using an automated machine learning solution such as TADA, professionals can easily get a first unbiased estimated valuation of a property according to different criteria. This prediction is made quickly, with great precision, which allows them to proceed with their business operations and focus on offering the best service to their customers rather than spending precious time on engineering property valuation.

    Sources

     A.Andonov, . M. A. Eichholtz, N. Kok Intermediated Investment Management in Private Markets: Evidence from Pension Fund Investments in Real Estate (July 31, 2014). Available at SSRN: https://ssrn.com/abstract=1996819 or http://dx.doi.org/10.2139/ssrn.1996819