Real Estate Valuation Algorithm Using Nonparametric Regression Methods

Abstract

Motivation: Real estate valuation is critical for various market participants and is influenced by numerous factors such as geographic location, property characteristics, and market trends. Traditional valuation methods often struggle to handle the complexity, scale, and dynamic nature of real estate markets. Automated valuation models (AVMs) have emerged as a promising alternative, offering scalability, speed, and adaptability. However, their effectiveness depends heavily on the ability to manage diverse data and model nonlinear relationships in valuation.

Aim: This paper presents a novel algorithm for real estate valuation based on nonparametric regression methods, including local-linear kernel and Nadaraya-Watson estimators. The algorithm is designed to handle missing data, incorporate multiple influencing factors with adjustable weights, and adapt to dynamic market conditions. It aims to provide accurate property valuations by leveraging a systematic and flexible approach to segment data, select nearest neighbors, and update prices based on market trends.

Results: The proposed algorithm demonstrates robustness to data errors, flexibility in handling variables measured on different scales, and the capacity to model nonlinear relationships. It effectively estimates property values using a comprehensive eight-stage process, from data preparation to quality assessment. The proposed algorithm is designed to be scalable and adaptable, making it a promising tool for addressing the challenges of real estate valuation across diverse contexts.