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

arXiv: https://arxiv.org/abs/2506.15723 · HTML: https://mlsanity.com/paper/2506.15723/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 131 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Sampling Permutations for Shapley Value Estimation](https://mlsanity.com/paper/2104.12199/) (2021)
- [Modeling and Analyzing Urban Networks and Amenities with OSMnx](https://mlsanity.com/paper/2505.00736/) (2025)
- [House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China](https://mlsanity.com/paper/2202.04358/) (2022)
- [An Optimal House Price Prediction Algorithm: XGBoost](https://mlsanity.com/paper/2402.04082/) (2024)

## Abstract (arXiv)

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.