Authors: Jing-Tang Tsay; Jerry T. Yang; Che-Chun Lin
Addresses: Department and Graduate Institute of Finance, National Taipei University of Business, Taiwan ' Department of Finance, National United University, Taiwan ' Department of Quantitative Finance National Tsing Hua University, Taiwan
Abstract: We propose an adjusted matching model to predict housing prices with actual transaction data for the real estate market in Taiwan. The lack of transparency in Taiwan's real estate market has resulted in information asymmetry between consumers and housing agencies for a long time until the 'actual price registration' online system took effect in August 2012. Utilising APR database, our pricing model encompasses the advantages of three commonly-used pricing methods: hedonic pricing, repeat sales, and matching methods, while mitigating their shortcomings. Numerical results show that our method performs better than hedonic pricing model in terms of prediction precision. Since the sampling period we select overlaps with the planning stage of 'consolidated housing and land taxes', our empirical results can reflect and quantify the impact of the proposed property taxation system on housing price before its implementation. Our study contributes to providing for real estate market a suitable pricing model in accordance with transaction time, place, and housing characteristics. The results of our study can also shed light on how to value the real estate properties included in an urban renewal program or the agricultural lands considered a change of land usage.
Keywords: actual price registration; hedonic pricing method; repeat sales method; matching method; agricultural lands; Taiwan.
International Journal of Agriculture Innovation, Technology and Globalisation, 2020 Vol.1 No.3, pp.207 - 225
Received: 23 Jul 2018
Accepted: 28 Jan 2019
Published online: 24 Mar 2020 *