Machine learning in house price analysis: regression models versus neural networks

Abstract

The problem addressed in this paper is automatic house price determination using multiple regression models and machine learning. In the practice of real estate appraisal, discussions about automated valuation (AVM) are increasingly common. Resistance to modern machine learning methods stems from a low level of knowledge about these methods and, as a result, an unawareness to what extent and where they can support the classical process of estimating property value. The problem is much broader, because it affects many aspects of the use of machine learning (including neural networks) in the broader real estate market. The process of real estate management at each stage generates huge information resources, which are used at different levels and to different extents by entities operating in the real estate market. One of such professional groups are real estate appraisers, for whom intelligent systems for monitoring the market and providing necessary data are becoming increasingly common and sought after. In this context, a comparative study of two models: multiple regression and neural networks has been carried out. Both methods were used to determine house prices, based on the same set of input data, and in the next step the effects of using these models for an additional group of objects with known characteristics and transaction prices were compared. The multivariate regression model obtained in the study was of medium quality, but sufficient for the purpose of comparative analysis. In the case of neural networks, the highest quality model was not obtained for the study sample, despite normalizing the variables to the required interval (0;1). In both models, the prices for the control sample were overestimated in most cases. However, this does not deny the relevance of further research and attempts to teach neural networks using larger data sets, especially in the case of properties other than typical residential units or land for residential development. Machine learning methods can be extremely useful especially in the processes of general valuation. In these processes, large sets of properties are estimated in a short period of time and with the same methods.