Prior information in econometric real estate appraisal: a mixed estimation procedure

Abstract

PurposeThe purpose of this paper is to present how prior knowledge about the impact of real estate features on value might be utilised in the econometric models of real estate appraisal. In these models, price is a dependent variable and real estate features are explanatory variables. Moreover, these kinds of models might support individual and mass appraisals.Design/methodology/approachA mixed estimation procedure was discussed in the research. It enables using sample and prior information in an estimation process. Prior information was provided by real estate experts in the form of parameter intervals. Also, sample information about the prices and features of undeveloped land for low-residential purposes was used. Then, mixed estimation results were compared with ordinary least squares (OLS) outcomes. Finally, the estimated econometric models were assessed with regard to both formal criteria and valuation accuracy.FindingsThe OLS results were unacceptable, mostly because of the low quality of the database, which is often the case on local, undeveloped real estate markets. The mixed results are much more consistent with formal expectations and the real estate valuations are also better for a mixed model. In a mixed model, the impact of each real estate feature could be estimated, even if there is no variability in the sample information. Valuations are also more precise in terms of their consistency with market prices. The mean error (ME) and mean absolute percentage error (MAPE) are lower for a mixed model.Originality/valueThe crucial problem in econometric property valuation is that it involves the unreliability of databases, especially on undeveloped, local markets. The applied mixed estimation procedure might support sample information with prior knowledge, in the form of stochastic restrictions imposed on parameters. Thus, that kind of knowledge might be obtained from real estate experts, practitioners, etc.

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“…Research efforts should focus on developing Explainable AI (XAI) techniques tailored for real estate valuation to enhance the interpretability and transparency of AI-driven valuation models. Additionally, exploring strategies for integrating diverse data sources and developing hybrid AI systems that combine machine learning with human expertise represents promising avenues for enriching valuation insights and improving decision-making (Doszyń, 2021).…”

Might expert knowledge improve econometric real estate mass appraisal?

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Econometric Models of Real Estate Prices with Prior Information. Mixed Estimation

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