MEASURING THE EFFECTS OF MARKETING EXPENSES AND EXTERNAL FACTORS ON HOUSING SALES TRANSACTIONS

Abstract

Purpose- In recent years, with the support of urban regeneration movements, the real estate sector has become one of the locomotive sectors in terms of economic and social development, particularly for the developing countries. When the real estate sector is examined, it is seen that the housing sector, which directly touches to the end user and is considered sometimes for use sometimes for the investment purposes, comes to the forefront. It is observed that the competition among the developer firms also increased in parallel with the investments made. In this study, the 2004-2017 period was examined, the statistical models were created to help the developers in developing the housing marketing strategies, the marketing strategies affecting the house sales trends and the external factors were highlighted. Our paper is the first academic study that identifies this relationship in Turkish housing market.

Methodology- Within the scope of this study; R programming language and Wilcoxon Rank was used to analyze the different housing marketing campaigns of one of the pioneer real estate firms in Turkey and their effect on the sales figures; the rank sum test was conducted; and, the VAR models were constructed and Impulse Response analysis, Pearson Correlation Coefficient were used for the relationship analyses.

Findings- According to the results of the study, it is seen that the social events, long-term holidays, rainfall and snowfall, campaigns of the competitors have no statistically significant effect on the net sales and gross income. However, it was determined that the Ramadan period and the digital marketing had a significant effect on the net sales and gross income. It is determined that the use of outdoor billboard, which is expected to affect the housing sales, was inversely proportional to the net sales and gross income, that is, it had a negative effect when applied.

Conclusion- It is thought that this study can be more improved as a result of including the followings into the model: more detailed classification of the social events, remodeling the Ramadan periods according to either they coincide with the summer month or the winter month, assessment of the effect of rainfall and snowfall considering the climate zone Turkey is in, more detailed analysis of the effect of the campaign of competitors, that the effect of digital marketing will be higher as the technology develops.

It is expected that the modeling of the findings reached in this study (or which will be detailed in later studies) by using an algorithm will provide a cost-benefit optimization.

Keywords

• Housing sector,marketing costs,marketing,housing sales transaction,modelling

References

1. Cengel, O. (2006). Emerging marketing techniques in the real estate sector and current implications. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 5(9), 125-131.
2. Chin, H. C., Quddus, M. A. (2003). Modeling count data with excess zeroes – an empirical application to traffic accidents. Sociological Methods & Research, 32(1), 90–116.
3. Cizmeci, F., Ercan, T. (2015). The effect of digital marketing communication tools in the creation brand awareness by housing companies. Megaron, 10(2), 149-161.
4. Kerby, D. S. (2014). The Simple Difference Formula: An Approach to Teaching Nonparametric Correlation 1. Comprehensive Psychology, 3(11).
5. Komurlu, R. et al. (2013). Drivers of residential developers’ marketing strategies based on buyer preferences. METU Journal of the Faculty of Architecture, 30(2), 1-16.
6. Miles, M., Berens, G. and Weiss, M. (2001), Real Estate Development: Principles and Process, Urban Land Institute, Washington, DC
7. Pesaran, H. Hashem, and Yongcheol Shin. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1), 17-29.
8. Polat, S., Ferman, M. (2015). An analysis of factors affecting understanding and applications of branded housing project marketing around the Istanbul metropolitan area. Journal of Management, Marketing and Logistics, 2(1), 24-36.

9. PWC, (2015, February) The World in 2050.
10. Stigler, S. M. (1989). Francis Galton's Account of the Invention of Correlation. Statistical Science, 4(2), 73–79
11. Terri, T. (2003). Fair Housing Marketing Strategy and Materials Development, Request for Proposals, Arizona Department of Housing, Phoenix, AZ.
12. TUIK (Turkish Statistical Institute). House Sales Statistics. Retrieved form http://www.tuik.gov.tr/PreTablo.do?alt_id=1056.
13. TUIK (Turkish Statistical Institute). Household Stats. Retrieved from http://www.tuik.gov.tr/PreHaberBultenleri.do?id=18624.
14. TUIK (Turkish Statistical Institute). Population Stats. Retrieved from http://www.tuik.gov.tr/PreHaberBultenleri.do?id=18616.
15. TUIK Retrieved from https://biruni.tuik.gov.tr/gosterge/?locale=en.
16. TUIK Retrieved from http://web.archive.org/web/20160104210603
17. Worldbank, Statistics about Turkey. Retrieved from http://data.worldbank.org/country/turkey.
18. Zeileis, A., Kleiber, C. and Jackman, S. (2008). Regression Models for Count Data in R, Journal of Statistical Software, July 2008, Volume 27, Issue 8:1-25.
19. Zivot, E. and Jiahui, W. (2006). Vector autoregressive models for multivariate time series. Modeling Financial Time Series with S-Plus®, 385-429.