Tag Archief van: Machine Learning Algorithms

Automated valuation models (AVMs) provide efficient means for local government to determine fair and equitable property taxes, for mortgage providers to limit risks, and for asset owners to make complex investment decisions. Traditionally, AVMs have been econometric models, such as linear regression models. However, recent advancements in the field of machine learning (ML) have opened up a new, and in many fields successful toolbox, providing additional methods for the same data, as well as approaches to access new sources of information and to create new variables.
An important distinction between traditionally applied methods and more recently introduced techniques lies in the structure definition of a model. Econometric models require a model specification – transformation of variables, selection of functional form, interaction effects, and distributional assumptions – prior to estimating parameter values, whereas most ML algorithms determine the model’s structure and parameter values simultaneously (Athey, 2018).
This fundamental difference has theoretical consequences that are naturally reflected in practical applications. The main goal of this paper is therefore to discuss how ML algorithms compare to econometric models for residential real estate valuation in theory and to show what these theoretical differences mean in practice.

In Section 2 we discuss the position of ML algorithms within the landscape of AVMs by comparing econometric models and ML algorithms from a theoretical perspective. Section 3 shows two different ML applications within residential property valuation to highlight the advantages and disadvantages of ML algorithms. Finally, Section 4 concludes and provides routes for future research.

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Authors: David Kroon and Marc Francke

Out-of-Sample House Price Prediction by Hedonic Price Models and Machine Learning Algorithms

In an illiquid market like the real estate market, market values are not readily available. Transactions are scarce and do not always reflect market value. As a consequence, appraisal values play an important role to inform agents in decision making, financial reporting and for property taxes. For example, appraisal values are used for property investment decisions and for providing mortgage loans. In a recent report De Nederlandsche Bank raises concerns about the quality and independency of appraisal values (Van der Molen and Nijskens, 2019). The authors show that one third of all appraisal values exactly match the transaction price, and in almost 60% the appraisal value is higher than the transaction price. Automated valuation models (AVMs) are less prone to potential client influence. However, in order to be accepted by a broad audience, AVMs need to be transparent, robust, explainable and they need to provide reliable predictions. In this research we address these issues. We compare traditional hedonic price models to more advanced machine learning algorithms and analyse the accuracy of out-of-sample predictions and variable importance. The research is based on almost all residential transaction prices in the Netherlands in 2017.

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Authors: Jeroen Beimer & Marc Francke