Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30132
Influence Analysis of Macroeconomic Parameters on Real Estate Price Variation in Taipei, Taiwan

Authors: Li Li, Kai-Hsuan Chu

Abstract:

It is well known that the real estate price depends on a lot of factors. Each house current value is dependent on the location, room number, transportation, living convenience, year and surrounding environments. Although, there are different experienced models for housing agent to estimate the price, it is a case by case study without overall dynamic variation investigation. However, many economic parameters may more or less influence the real estate price variation. Here, the influences of most macroeconomic parameters on real estate price are investigated individually based on least-square scheme and grey correlation strategy. Then those parameters are classified into leading indices, simultaneous indices and laggard indices. In addition, the leading time period is evaluated based on least square method. The important leading and simultaneous indices can be used to establish an artificial intelligent neural network model for real estate price variation prediction. The real estate price variation of Taipei, Taiwan during 2005 ~ 2017 are chosen for this research data analysis and validation. The results show that the proposed method has reasonable prediction function for real estate business reference.

Keywords: Real estate price, least-square, grey correlation, macroeconomics.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2022047

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 339

References:


[1] Case B., Clapp J., Dubin R. and Rodriguez M., “Modeling spatial and temporal house price patterns: A comparison of four models,” Journal of Real Estates Finance and Economic, Vol. 29 no. 2, pp. 167-191, 2004.
[2] Kim K. and Park J., “Segmentation of the housing market and its determinants: Seoul and its neighboring new towns in Korea, Australian Geographer, Vol. 36 no. 2, pp. 221-232, 2005.
[3] Robin Dubin, “Predicting House Prices Using Multiple Listing Data,” The Journal of Real Estate Finance and Economic, Vol. 17, no. 1, pp. 39-59, 1998.
[4] Hasan Selim, “Determinants of house prices in Turkey: Hedonic regression versus artificial neural network,” Expert Systems with Applications, Vol. 36, pp. 2843-2852, 2009.
[5] Jian-Guo Liu, Xiao-Li Zhang and Wei-Ping Wu, “Application of Fuzzy neural network for Real Estate Prediction,’ Advances in Neural Networks, Volume 3973 of the series Lecture Notes in Computer Science, pp. 1187-1191, 2006.
[6] Glenn R. Mueller,”Real Estate Rental Growth Rates at Different Points in the Physical Market Cycle”, Journal of Real Estate Research, Vol. 18, no. 1, pp. 131-150, 1999.
[7] Reinaldo C. Garcia, Javier Contreras, Marco van Akkeren, and João Batista C. Garcia, “A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices,” IEEE Transactions on Power Systems, Vol. 20, no. 2, pp. 867-874, 2005.
[8] Chen-Wei Pen and Kim-W o Chang, “The influence study of Macro-economic on Real Estate Climate,” NSC Research Journal, Literature and Social Science, Vol. 10, no. 3, pp. 330-343, (in Chinese), 2000.