Alternative methods for measuring the influence of location in hedonic pricing models

Abstract

The effects of location play a crucial role in the real estate market, encompassing aspects of accessibility and neighborhood. However, these are elements that are not directly measurable. There are traditional ways to consider location, usually through subjective measures based on professional experience, through proxy variables. Understanding these elements is vital for estimating real estate values, whether for legal, commercial, or tax purposes. Furthermore, seeking more objective options is a relevant issue to broaden the justification of estimated values and to enable the development of mass appraisal models. This article proposes and evaluates alternative solutions based on statistics, machine learning, and geostatistics to estimate location. A study was conducted using market data from Novo Hamburgo, southern Brazil, verifying the feasibility of the options presented. Satisfactory statistical results demonstrate the viability of the proposed approach.

Keywords

• Location quality
• Hedonic modeling
• Machine Learning
• Fuzzy logic
• Kriging