Share:


Establishing dynamic impact function for house pricing based on surrending multi-attributes: evidence from Taipei city, Taiwan

    Jieh-Haur Chen Affiliation
    ; Li-Ren Yang Affiliation
    ; Vidya Trisandin Azzizi Affiliation
    ; Eric Chu Affiliation
    ; Hsi-Hsien Wei Affiliation

Abstract

The objective of the research is aimed for a solution that is to establish the dynamic impact function of surrounding multi-attribute for house pricing. It is also able to measure the ripple effect and allows the hedonic parameter estimates to vary from point-to-point. A comprehensive literature review is carried out to obtain an adequate theoretical basis for the corresponding hypothesis and concepts. The proposed dynamic impact function for multi- attributes is then constructed based on the characteristics of surrounding facilities. Adopting the convenience sampling criteria of 95% confidence level on the data sampling and 10% limit of error in a 5−95% proportion, we collect the empirical data of 39 yearly house sales in the investigated urban areas of Taipei city focusing on housing prices and then utilize them for evaluating and adjusting the function. The actual house price and that of proposed function affected by Mass Rapid Transit (MRT) stations are analysed, resulting in the correlation coefficient at 0.946 (single attribute) and 0.944 (multi-attribute), respectively. The findings support that proposed function can highly represent the house pricing pattern and be an accurate tool for appraisers.

Keyword : house pricing theory, impact function, multi-attribute, financial engineering, property management

How to Cite
Chen, J.-, H., Yang, L.-, R., Azzizi, V. , T., Chu, E., & Wei, H.-, H. (2020). Establishing dynamic impact function for house pricing based on surrending multi-attributes: evidence from Taipei city, Taiwan. International Journal of Strategic Property Management, 24(2), 119-129. https://doi.org/10.3846/ijspm.2020.11096
Published in Issue
Feb 14, 2020
Abstract Views
53
PDF Downloads
38
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Agyemang, F. O., Asamoah, P. K. B., & Obodai, J. (2018). Changing family systems in Ghana and its effects on access to urban rental housing: a study of the Offinso Municipality. Journal of Housing and the Built Environment, 33(4), 893-916. https://doi.org/10.1007/s10901-018-9604-7

Alexander, C., & Barrow, M. (1994). Seasonality and cointegration of regional house prices in the UK. Urban Studies, 31, 1667-1689. https://doi.org/10.1080/00420989420081571

Antonakakis, N., Chatziantoniou, I., Floros, C., & Gabauer, D. (2018). The dynamic connectedness of UK regional property returns. Urban Studies, 55(14), 3110-3134. https://doi.org/10.1177/0042098017739569

Badarinza, C., & Ramadorai, T. (2018). Home away from home? Foreign demand and London house prices. Journal of Financial Economics, 130(3), 532-555.
https://doi.org/10.1016/j.jfineco.2018.07.010

Bahmani-Oskooee, M., & Wu, T.-P. (2018). Housing prices and real effective exchange rates in 18 OECD countries: a bootstrap multivariate panel Granger causality. Economic Analysis and Policy, 60, 119-126. https://doi.org/10.1016/j.eap.2018.09.005

Bhargava, B., Wu, X., Lu, Y., & Wang, W. (2004). Integrating heterogeneous wireless technologies: a cellular aided mobile ad hoc network (CAMA). Mobile Networks and Applications, 9(4), 393-408. https://doi.org/10.1023/B:MONE.0000031606.04846.fa

Chen, P.-F., Chien, M.-S., & Lee, C.-C. (2011). Dynamic modeling of regional house price diffusion in Taiwan. Journal of Housing Economics, 20(4), 315-332. https://doi.org/10.1016/j.jhe.2011.09.002

Chen, J.-H., Ong, C. F., Zhang, L., & Hsu, S.-C. (2017). Forecasting spatial dynamics of the housing market using support vector machine. International Journal of Strategic Property Management, 21(3), 273-283. https://doi.org/10.3846/1648715X.2016.1259190

Chen, J.-H., Yang, L.-R., Su, M.-C., & Lin, J.-Z. (2010). A rule extraction based approach in predicting derivative use for financial risk hedging by construction companies. Expert Systems with Applications, 37(9), 6510-6514. https://doi.org/10.1109/ICIII.2011.101

Chen, J.-H., & Hsu, S. C. (2008). Quantifying impact factors of corporate financing: engineering consulting firms. Journal of Management in Engineering, 24(2), 96-104.
https://doi.org/10.1061/(ASCE)0742-597X(2008)24:2(96)

Des Rosiers, F., Lagana, A., & Theriault, M. (2001). Size and proximity effects of primary schools on surrounding house values. Journal of Property Research, 18(2), 149-168.
https://doi.org/10.1080/09599910110039905

Droes, M. I., & Francke, M. K. (2018). What causes the positive price-turnover correlation in European housing markets. Journal of Real Estate Finance and Economics, 57(4), 618-646. https://doi.org/10.1007/s11146-017-9602-7

Du, Q. Y., Wu, C., Ye, X. Y., Ren, F., & Lin, Y. J. (2018). Evaluating the effects of landscape on housing prices in urban China. Tijdschrift Voor Economischie en Sociale Geografie, 109(4), 525-541. https://doi.org/10.1111/tesg.12308

Gholipour, H. F., Al-Mulali, U., Lee, J. Y. M., & Hakim, A. M. (2016). Dynamic relationship between house prices in Malaysia’s major economic regions and Singapore house prices. Regional Studies, 50, 657-670. https://doi.org/10.1080/00343404.2014.928408

Gholipour, H. F., & Lean, H. H. (2017). Ripple effect in regional housing and land markets in Iran: implications for portfolio diversification. International Journal of Strategic Property Management, 21(4), 331-345. https://doi.org/10.3846/1648715X.2016.1272010

Gu, G. (2018). Hedonic price ripple effect and consumer choice: Evidence from new homes. Journal of Advanced Computational Intelligence and Intelligent Informatics, 22(6), 809-816. https://doi.org/10.20965/jaciii.2018.p0809

Hill, R. J., & Scholz, M. (2018). Can geospatial data improve house price indexes? A hedonic imputation approach with splines. Review of Income and Wealth, 64(4), 737-756. https://doi.org/10.1111/roiw.12303

Hui, H. C. (2010). House price diffusions across three urban areas in Malaysia. International Journal of Housing Markets and Analysis, 3, 369-379. https://doi.org/10.1108/17538271011080664

Jonkman, A., Janssen-Jansen, L., & Schilder, F. (2018). Rent increase strategies and distributive justice: the socio-spatial effects of rent control policy in Amsterdam. Journal of Housing and the Built Environment, 33(4), 653-673.
https://doi.org/10.1007/s10901-017-9573-2

Kopczewska, K., & Lewandowska, A. (2018). The price for subway access: spatial econometric modelling of office rental rates in London. Urban Geography, 39(10), 1528-1554. https://doi.org/10.1080/02723638.2018.1481601

Lean, H. H., & Smyth, R. (2013). Regional house prices and the ripple effect in Malaysia. Urban Studies, 50(5), 895-922. https://doi.org/10.1177/0042098012459582

Lee, C. C., Liang, C. M., Chen, J. Z., & Tung, C. H. (2018). Effects of the housing price to income ratio on tenure choice in Taiwan: forecasting performance of the hierarchical generalized linear model and traditional binary logistic regression model. Journal of Housing and the Built Environment, 33(4), 675-694. https://doi.org/10.1007/s10901-017-9572-3

Li, M., & Brown, H. (1980). Micro-neighborhood externalities and hedonic housing prices. Land Economics, 56(2), 125-141. https://doi.org/10.2307/3145857

Mastromonaco, R., & Maniloff, P. (2018). An examination of geographic heterogeneity in price effects of superfund site remediation. Economics Letters, 171, 23-28. https://doi.org/10.1016/j.econlet.2018.06.026

McCord, M. J., Davis, P. T., Bidanset, P., McCluskey, W., McCord, J., Haran, M., & MacIntyre, S. (2018). House prices and neighbourhood amenities: beyond the norm. International Journal of Housing Markets and Analysis, 11(2), 263-289. https://doi.org/10.1108/IJHMA-04-2017-0043

McCord, M. J., MacIntyre, S., Bidanset, P., Lo, D., & Davis, P. (2018). Examining the spatial relationship between environmental health factors and house prices. Journal of European Real Estate Research, 11(3), 353-398. https://doi.org/10.1108/JERER-01-2018-0008

Meen, G. (1999). Regional house prices and the ripple effect: a new interpretation. Housing Studies, 14, 733-753.
https://doi.org/10.1080/02673039982524

Oikarinen, E. (2008). The diffusion of housing price movements from center to surrounding areas. Journal of Housing Research, 15, 1-28.

Oikarinen, E. (2014). Is urban land price adjustment more sluggish than housing price adjustment? Empirical evidence. Urban Studies, 51, 686-1706.
https://doi.org/10.1177/0042098013497409

Taltavull de la Paz, P., & McGreal, S. (2019). A re-assessment of house price indices: evidence from the Spanish market.
International Journal of Strategic Property Management, 23(1), 23-35. https://doi.org/10.3846/ijspm.2019.6366

Wen, K.-C., & Chang, S.-S. (2014). An environmental behavioural study of crowd flow transformation at Taipei MRT station. Procedia Environmental Sciences, 22, 43-60. https://doi.org/10.1016/j.proenv.2014.11.005

Wen, H. Z., Chu, L. H., Zhang, J. F., & Xiao, Y. (2018). Competitive intensity, developer expectation, and land price: evidence from Hangzhou, China. Journal of Urban Planning and Development, 144(4), 04018040. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000490

Wu, Y., Sah, V., & Tidwell, A. (2018). Housing preferences of Asian and Hispanic/Latino immigrants in the United States: a melting pot or salad bowl. Real Estate Economics, 46(4), 783-835. https://doi.org/10.1111/1540-6229.12178

Zhang, J. J., Fu, M. C., Chen, J., Chu, P. P., & Zhang, C. C. (2018). Variations in mine subsidence-disturbed residential land price: case study of critical determinants and spatial relationships in the Nanhu ecoregion of Tangshan, China. Journal of Urban Planning and Development, 144(3), 05018012. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000457

Zhang, L., Zhou, J. T., Hui, E. C. M., & Wen, H. Z. (2019). The effect of a shopping mall on housing prices: a case study in HangZhou. International Journal of Strategic Property Management, 23(1), 65-80. https://doi.org/10.3846/ijspm.2019.6360

Zhang, L., & Yi, Y. (2018). What contributes to the rising house prices in Beijing? A decomposition approach. Journal of Housing Economics, 41, 72-84. https://doi.org/10.1016/j.jhe.2018.04.003