Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices

Yunjong Kim,Mun Yong Yi,Seungwoo Choi

doi:10.3390/su12145679

Yunjong Kim, Mun Yong Yi + Show 1 more

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https://doi.org/10.3390/su12145679

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Abstract

In this paper, we propose a novel procedure designed to apply comparable sales method to the automated price estimation of real estates, in particular, that of apartments. Apartments are the most popular residential housing type in Korea. The price of a single apartment is influenced by many factors, making it hard to estimate accurately. Moreover, as an apartment is purchased for living, with a sizable amount of money, it is mostly traded infrequently. Thus, its past transaction price may not be particularly helpful to the estimation after a certain period of time. For these reasons, the up-to-date price of an apartment is commonly estimated by certified appraisers, who typically rely on comparable sales method (CSM). CSM requires comparable properties to be identified and used as references in estimating the current price of the property in question. In this research, we develop a procedure to systematically apply this procedure to the automated estimation of apartment prices and assess its applicability using nine years’ real transaction data from the capital city and the most-populated province in South Korea and multiple scenarios designed to reflect the conditions of low and high fluctuations of housing prices. The results from extensive evaluations show that the proposed approach is superior to the traditional approach of relying on real estate professionals and also to the baseline machine learning approach.

Highlights

Since the subprime mortgage crisis [1], which was caused by over-inflated house prices, the valuation of real estate prices has emerged as a critical economic activity directly tied to national economic health [2]
The largest bank in Korea, KB Kookmin Bank, relies on a massive number of these real estate agents to produce and update its assessment of those real estate properties every week, sharing that information with other major banks across the country, which is used for determining the maximum amount of mortgage loan associated with the real estate property
For Seoul and Gyeonggi, adding the full set of comparable sales method (CSM)-derived price features showed the highest improvement over the baseline in comparison to the conditions where only the nearby apartment transaction or similar price transaction features were added, confirming that the additional features based on CSM have positive effects on improving the prediction accuracy of apartment prices, over and above the regular machine learning approach, and the nearby apartment transaction features and the similar price transaction features have independent effects, lending themselves to greater effects when combined together

Summary

Introduction

Since the subprime mortgage crisis [1], which was caused by over-inflated house prices, the valuation of real estate prices has emerged as a critical economic activity directly tied to national economic health [2]. Computational approaches to real estate valuation are much more efficient and free from individual human biases. These automated approaches are likely to become essential for the design and operation of smart cities [4,5]. The past selling price of a house may not be helpful in identifying its current value after a certain period of time To overcome these limitations and difficulties, we propose a new automated procedure developed on the basis of comparable sales method [6,7], which can be applied to any real estate valuation settings, and test its effectiveness in comparison to human expert estimations, involving the most populated areas in Korea using two different scenarios

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