Bagaskara Bagaskara, Fatima Putri Prativi

Model development for estimating sales adjusment magnitude in real estate appraisal using hedonic price model and paired data analysis

Introduction

Model development for estimating sales adjusment magnitude in real estate appraisal using hedonic price model and paired data analysis. Develops a model to estimate sales adjustment magnitude in real estate appraisal using hedonic price models and paired data analysis. Helps appraisers determine accurate adjustments for heterogeneous markets and advances real estate valuation science.

Abstract

This study aims to develop a model that can identify a reference for determining the adjustment magnitude in the market approach used in appraisal practice. Currently, it is challenging for appraisers to accurately determine the extent of these adjustments. Data collection in this study employed purposive sampling, ensuring that the criteria and specifications closely mirrored the data collection process in appraisal practices. The researcher selected three districts to capture any possible variations in location affecting the adjustment magnitude. The collected data was regressed to build a hedonic price model, which was later analyzed using the paired data analysis method to meet ideal model conditions and specifications. The results of this study demonstrate that the adjustment magnitude in the market approach can be identified from price differences resulting from variations in a single attribute. The highly heterogeneous housing market conditions in Indonesia present significant challenges for conducting paired data analysis in a practical context. Artificial conditions were required to meet the specifications for paired data analysis. The implication of the study that appraisers in determining adjustments in the market approach. Additionally, the model/procedure developed in this study can be applied in research across different contexts and objects, contributing to the advancement of real estate valuation science.

Review

The paper, "Model development for estimating sales adjustment magnitude in real estate appraisal using hedonic price model and paired data analysis," addresses a critical practical challenge in real estate appraisal: the accurate determination of adjustment magnitudes in the market approach. The study proposes a model combining hedonic price modeling with paired data analysis to provide a more objective basis for these adjustments, which appraisers currently struggle with. This objective is highly relevant to the field, aiming to enhance the reliability and consistency of real estate valuations, particularly within the context of heterogeneous markets like Indonesia. The premise of developing a systematic procedure to quantify attribute-specific price differences holds significant potential for improving appraisal practice. The methodology employs a sound combination of techniques. The hedonic price model is well-suited for disaggregating property prices into the values of their individual attributes, while paired data analysis, when conditions allow, provides a robust method for isolating the impact of a single attribute variation. The authors' recognition of the highly heterogeneous housing market in Indonesia and the subsequent need for "artificial conditions" to meet paired data analysis specifications is a candid and important acknowledgment. Purposive sampling across three districts to capture location variations demonstrates a thoughtful approach to data collection, aiming to mirror real-world appraisal scenarios. The study correctly identifies that adjustment magnitudes can be derived from price differences stemming from single attribute variations, aligning with core appraisal principles. The stated implications for appraisers, offering a structured approach to determining market adjustments, are valuable. Furthermore, the potential applicability of the developed model/procedure to research in diverse contexts and objects suggests a broader contribution to real estate valuation science. However, the reliance on "artificial conditions" to enable paired data analysis in a highly heterogeneous market presents a significant practical limitation that requires further elucidation within a full paper. While a necessary step for the current methodology, it raises questions about the generalizability and practical implementation of the model without such idealizations in real-world, complex scenarios. Future research could productively explore methods to bridge this gap, perhaps by developing more sophisticated statistical techniques that can robustly handle high heterogeneity while still isolating attribute-specific adjustments, or by providing clearer guidelines on how appraisers can navigate or create these "artificial conditions" in practice.

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