Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation

TL;DR

This paper reviews modern, interpretable machine learning models for property market valuation, demonstrating their effectiveness and comparability to black-box methods through practical examples in the Primorye region.

Contribution

It introduces a comprehensive approach to building interpretable property valuation models, combining classical regression, geostatistics, and decision trees, tailored for real market data.

Findings

Interpretable models achieve similar accuracy to Random Forests.

Combining linear regression with kriging effectively models land parcel prices.

RuleFit method is suitable for flats with multiple objects at the same location.

Abstract

In this paper, we review modern approaches to building interpretable models of property markets using machine learning on the base of mass valuation of property in the Primorye region, Russia. There are numerous potential difficulties one could encounter in the effort to build a good model. Their main source is the huge difference between noisy real market data and ideal data usually used in tutorials on machine learning. This paper covers all stages of modeling: collection of initial data, identification of outliers, search and analysis of patterns in the data, formation and final choice of price factors, building of the model, and evaluation of its efficiency. For each stage, we highlight potential issues and describe sound methods for overcoming emerging difficulties on actual examples. We show that the combination of classical linear regression with kriging (interpolation method of geostatistics) allows to build an effective model for land parcels. For flats, when many objects are attributed to one spatial point, the application of geostatistical methods becomes problematic. Instead, we suggest linear regression with automatic generation and selection of additional rules on the base of decision trees, so called the RuleFit method. We compare the performance of our inherently interpretable models with well-proven "black-box" Random Forest method and demonstrate similar results. Thus we show, that despite such a strong restriction as the requirement of interpretability which is important in practical aspects, for example, legal matters, it is still possible to build effective models of real property markets.