A Machine Learning Approach to Price Indices: Applications in Real Estate

30 Pages Posted: 27 Oct 2020

See all articles by Felipe Dutra Calainho

Felipe Dutra Calainho

University of Amsterdam, Faculty of Economics and Business (FEB), Students

Alex Van de Minne

University of Connecticut - School of Business

Marc Francke

University of Amsterdam - Faculty of Economics and Business (FEB); Ortec Finance

Date Written: September 9, 2020

Abstract

This paper proposes a methodology for using machine learning regression models to create price indices. In our study we developed six commercial real estate price indeces for the city of New York from year 2000 to 2019. The regression models used in this study are eXtreme Gradient Boosting Tree (XGBT), Support Vector Regression (SVR) and averaged Neural Networks (avNNet). The benchmark for comparison of the results is Ordinary Least Squares (OLS).

There are two main index methodologies, a chained, where the index is constructed using out-of-sample data, and a pooled, where the index is constructed using in-sample data. The two main index methodologies can be divided into sub categories that utilizes a Paasche and Laspeyres like index formulations.

Another important factor included in this study is the size, in years, of the optimal training window for building the index. The results show that the machine learning approach produced, overall, lower estimation errors. Nevertheless, these lower estimation errors not always constitutes a more stable index. Additionally, all models, including the benchmark, are sensitive to the training window size regarding the out-of-sample estimation error.

Keywords: real estate price indexes, machine learning, AVMs

Suggested Citation

Dutra Calainho, Felipe and Van de Minne, Alex and Francke, Marc, A Machine Learning Approach to Price Indices: Applications in Real Estate (September 9, 2020). Available at SSRN: https://ssrn.com/abstract=3689632 or http://dx.doi.org/10.2139/ssrn.3689632

Felipe Dutra Calainho (Contact Author)

University of Amsterdam, Faculty of Economics and Business (FEB), Students ( email )

Amsterdam
Netherlands
+31629327692 (Phone)

Alex Van de Minne

University of Connecticut - School of Business ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States

Marc Francke

University of Amsterdam - Faculty of Economics and Business (FEB) ( email )

Plantage Muidergracht 12
Amsterdam, 1018 TV
Netherlands

HOME PAGE: http://www.uva.nl/en/contact/staff/item/m.k.francke.html?f=francke

Ortec Finance ( email )

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Amsterdam, 1043 CP
Netherlands
+ 31 20 7009 700 (Phone)
+ 31 20 7009 701 (Fax)

HOME PAGE: http://www.ortec-finance.com

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