Bayesian statistical estimation in real estate price modeling: a comparative study with traditional regression

Ammar  kuti Nasser

doi:10.5269/bspm.77649

Ammar kuti Nasser Mustansiriyah University

Abstract

Real estate price modeling is a key process in the valuation of property as well as in the financial planning and decision making when it comes to investing. Estimating price using Multiple Linear Regression (MLR) and Generalized Linear Model (GLM) techniques is currently the common practice among the experts, but these techniques fail to capture complexities of the real estate markets. The study assesses Bayesian statistical estimation as the state-of-the-art alternative that will maximize predictions based on a theory of previous distributions coupled with MCMC simulation. The research provides an assay on the performance of traditional regression models and Bayesian re-gression models and compares it with BIC (Bayesian Information Criterion) AIC (Akaike Information Criterion) and R^2 measure. The use of pre-existing market knowledge in Bayesian estimation results in improved performance of model fitting and produces improved predictive reliability as per findings. The study introduces Bayesian modelling methods as the improvement of the real estate price analysis resulting in improved property market decision support.

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