Machine learning study on real estate massive assessments: pseudo self comparison method

Abstract

however, one limitation of the model is that it does not consider the volatility of the real estate market.

To address this, we propose the pseudo self comparison method (PSCM). A pseudo self is defined as a real estate property with mostly the same hedonic features and similar price volatility as the target property. Examples include housing properties with different numbers of stories but the same floor plan, such as apartments and condominiums. The previous transaction price of the pseudo self and changes in the real estate market during the period of previous transaction were used as variables. To compare the proposed model with the HPM, we constructed a dataset of apartment transactions in Seoul and its surrounding region, Gyeonggi, and compared the price estimation results. The proposed technique was able to reduce the mean average percentage error by approximately one-fifth. Moreover, the error in price estimation did not significantly increase even when there was an increase in the volatility in the real estate market, demonstrating the model’s robustness.

Although the PSCM showed high estimation accuracy for real estate prices, it is difficult to apply it to multiplex houses and detached houses, whose hedonic features are distinctive. Therefore, we expanded this approach to develop a generalized PSCM. This method adds a module that searches for pseudo self candidates (PSCs) and a module that searches for similar transactions among the previous transactions of the PSCs. The first module applies a two-step clustering process

an initial cluster is formed based on the locational proximity context, after which a sub-cluster is formed within it based on the price context. For the second module, two previous transactions are matched based on the PSCs and then input into a deep learning-based similar transaction search module, after which the model is trained by predicting the dissimilarity score. Using the two proposed modules, the pseudo self’s previous transactions were selected even for real estate properties with completely different hedonic features, based on which PSCM variables were generated. The newly proposed approach outperformed the HPM in terms of price estimation ability, and the price estimation model combined with the HPM achieved an even higher performance. The results suggest that our proposed method adequately accounts for factors that are not considered in the existing HPM.

To conclude, the proposed PSCM regards property values in the same manner as real estate appraisers while also accounting for changes in the market

Volatility in real estate values greatly impacts the national economy and can also act as a warning signal for financial crises. As such, a wide range of studies have been conducted to accurately estimate the transaction prices of real estate properties. The hedonic price model (HPM) is a long-used methodology for estimating real estate values and is still extensively used across various fields today. It estimates the transaction price based on the structural characteristics and living conditions of the real estate property, as well as the characteristics of the surrounding environment. The HPM is advantageous for analyzing the relationship between the characteristics and price of a property

however, it also minimizes the subjective interference from the appraiser that is required for the actual evaluation and achieves high price estimation performance using only an automated approach. Therefore, the proposed PSCM is a suitable framework for mass valuation models of real estate properties.