A roadmap to determine the important factors of the house value: a case study by using actual price registration data of Taipei housing transactions
Main Article Content
Abstract
Downloads
Article Details
1. Proposal of Policy for Free Access Periodics
Authors whom publish in this magazine should agree to the following terms:
a. Authors should keep the copyrights and grant to the magazine the right of the first publication, with the work simultaneously permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 that allows the sharing of the work with recognition of the authorship of the work and initial publication in this magazine.
b. Authors should have authorization for assuming additional contracts separately, for non-exclusive distribution of the version of the work published in this magazine (e.g.: to publish in an institutional repository or as book chapter), with recognition of authorship and initial publication in this magazine.
c. Authors should have permission and should be stimulated to publish and to distribute its work online (e.g.: in institutional repositories or its personal page) to any point before or during the publishing process, since this can generate productive alterations, as well as increasing the impact and the citation of the published work (See The Effect of Free Access).
Proposal of Policy for Periodic that offer Postponed Free Access
Authors whom publish in this magazine should agree to the following terms:
a. Authors should keep the copyrights and grant to the magazine the right of the first publication, with the work simultaneously permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 [SPECIFY TIME HERE] after the publication, allowing the sharing of the work with recognition of the authorship of the work and initial publication in this magazine.
b. Authors should have authorization for assuming additional contracts separately, for non-exclusive distribution of the version of the work published in this magazine (e.g.: to publish in institutional repository or as book chapter), with recognition of authorship and initial publication in this magazine.
c. Authors should have permission and should be stimulated to publish and to distribute its work online (e.g.: in institutional repositories or its personal page) to any point before or during the publishing process, since this can generate productive alterations, as well as increasing the impact and the citation of the published work (See The Effect of Free Access).
d. They allow some kind of open dissemination. Authors can disseminate their articles in open access, but with specific conditions imposed by the editor that are related to:
Version of the article that can be deposited in the repository:
Pre-print: before being reviewed by pairs.
Post-print: once reviewed by pairs, which can be:
The version of the author that has been accepted for publication.
The editor's version, that is, the article published in the magazine.
At which point the article can be made accessible in an open manner: before it is published in the magazine, immediately afterwards or if a period of seizure is required, which can range from six months to several years.
Where to leave open: on the author's personal web page, only departmental websites, the repository of the institution, the file of the research funding agency, among others.
References
ACCIANI, C.; FUCILLI, V.; SARDARO, R. (2011) Data Mining in Real Estate Appraisal: A Model Tree and Multivariate Adaptive Regression Spline Approach. Aestimum, v. 58, p. 27-45.
BAHIA, I. S. H. (2013) A Data Mining Model by Using ANN for Predicting Real Estate Market: Comparative Study. International Journal of Intelligence Science, v. 3, n. 4. p. 162-169.
BREIMAN, L.; FRIEDMAN, J. H.; OLSHEN, R. A.; STONE, C. J. (1984) Classification and Regression Trees, Belmont, CA: Wadsworth.
BREIMAN, L. (2001) Random Forests. Machine Learning, v. 45, n. 1, p. 5-32.
BRACKE, P. (2015) House Prices and Rents: Microevidence From A Matched Data Set in Central London. Real Estate Economics, v. 43, n. 2, p. 403-431.
COAKLEY, J. R.; BROWN, C. E. (2000) Artificial Neural Networks in Accounting and Finance: Modeling Issues. International Journal of Intelligent Systems in Accounting, Finance and Management, v. 9, n. 2, p. 119-144.
CORTEZ, P. (2016) Package ‘rminer’. Available: https://cran.r-project.org/web/packages/rminer/rminer.pdf. Access: 2th September, 2016.
DEL CACHO, C. (2010) A Comparison of Data Mining Methods for Mass Real Estate Appraisal, n. 27378. Munich Personal RePEc Archive.
DELMENDO, L. C. (2016) Taiwanese House Prices Continue to Fall Due to Harsh Taxes. Retrieved on September 16, 2016, Available: http://www.globalpropertyguide.com/Asia/Taiwan/Price-History.
FAN, G. Z.; ONG, S. E.; KOH, H. C. (2006) Determinants of House Price: A Decision Tree Approach. Urban Studies, v. 43, n. 12, p. 2301-2315.
FIK, T. J.; LING, D. C.; MULLIGAN, G. F. (2003) Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach. Real Estate Economics, v. 31, n. 4, p. 623-646.
FONG, S.; WAH, Y. B. (2013) A Prediction Model for Forecasting the Trend of Macau Property Price Movements and Understanding the Influential Factors. Journal of Emerging Technologies in Web Intelligence, v. 5, n. 2, p. 122-131.
GAN, V.; AGARWAL, V.; KIM, B. (2015) Data Mining Analysis and Predictions of Real Estate Prices. Issues in Information Systems, v. 16, n. 4, p. 30-36.
GOODMAN, A. C. (1978) Hedonic Prices, Price Indices and Housing Markets. Journal of Urban Economics, v. 5, n. 4, p. 471-484.
JAMES, G.; WITTEN, D.; HASTIE, T.; TIBSHIRANI, R. (2013) An Introduction to Statistical Learning, New York: Springer.
KASS, G. V. (1980) An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, v. 29, n. 2, p. 119-127.
KUHN, M.; WESTON, S.; DEEFER, C.; COUTLER, N. (2016) Cubist Models for Regression, Available: https://cran.r-project.org/web/packages/Cubist/vignettes/cubist.pdf. Access: 10th December, 2016.
MAGIDSON, J. (1994) The CHAID Approach to Segmentation Modeling: Chi-squared Automatic Interaction Detection, in: BAGOZZI, R. P. (Ed.), Advanced Methods of Marketing Research. Malden (Mass. US): Blackwell Business, p. 118-159.
MANVILLE, M. (2013) Parking Requirements and Housing Development: Regulation and Reform in Los Angeles. Journal of the American Planning Association, v. 79, n. 1, p. 49-66.
MULLEY C. (Ed.), Parking: Issues and Policies. United Kingdom: Emerald Publishing, p. 87-113.
MUNUSAMY, M.; MUTHUVEERAPPAN, C.; BABA, M.; ABDULLAH, M. N.; ASMONI, M. (2015). An Overview of the Forecasting Methods Used in Real Estate Housing Price Modelling. Jurnal Teknologi, v. 73, n. 5, p. 189-193.
QUINLAN, J. R. (1986) Induction of Decision Trees. Machine Learning, v. 1, p. 81-106.
QUINLAN, J. R. (1992) C4. 5: Programming for Machine Learning, San Mateo, CA: Morgan Kauffmann.
SHOUP, D. (2014) The High Cost of Minimum Parking Requirements, in: ISON, S.;
SIRMANS, G. S.; MACDONALD, L.; MACPHERSON, D. A.; ZIETZ, E. N. (2006) The Value of Housing Characteristics: A Meta Analysis. The Journal of Real Estate Finance and Economics, v. 33, n. 3, p. 215-240.
WELCH, T. F.; GEHRKE, S. R.; WANG, F. (2016) Long-term Impact of Network Access to Bike Facilities and Public Transit Stations on Housing Sales Prices in Portland, Oregon. Journal of Transport Geography, v. 54, p. 264-272.
WITTEN, I. H.; FRANK, E. (2005) Data Mining: Practical Machine Learning Tools and Techniques, 5 ed. Boston, MA: Morgan Kaufmann.
WOODS, E.; KYRAL, E. (1997) Ovum Evaluates Data Mining, London: Ovum.
XIAO, Y.; ORFORD, S.; WEBSTER, C. J. (2016) Urban Configuration, Accessibility, and Property Prices: A Case Study of Cardiff, Wales. Environment and Planning B: Planning and Design, v. 43, n. 1, p. 108-129.