Housing demand prediction in Selangor

Housing provision is one of the important sectors in ensuring socio-economic stability in Selangor as well as direct contributor to its economic growth. Its populations increased from 4.2 million in year 2000 to 6.3 million people in year 2016 affected the demand for housing. Thus, the sufficient houses should be provided to the next generations. The goal of the study is to develop housing demand prediction model based on 4 categories of housing in Selangor which are low cost, low-medium cost, medium cost and high cost houses. This study was focused in Selangor because the state was recorded highest residential after Johor and Penang. The aim was to determine housing demand for 4 categories of housing based on household formation. The study used Census Data 2010 from Department of Statistic Malaysia to determine the headship rate in Selangor using Headship Rate Method based on household formation. Then, a questionnaire was conducted on 415 respondents to determine the choice probability of selecting housing type by using Multinomial Logit (MNL) analysis in producing a Choice Probabilities (CP) Model. Result showed that low medium cost housing was the most preferable with the highest value of 0.899. Double Exponential Smoothing (DES) method was used to predict population for 14 level age groups from years 1970 until years 2020 based on Quick Population Info‟s data from the Department of Statistics Malaysia. Then total housing demand for 4 categories housing were determined by multiplying population with headship. This prediction model was validated using Mean Absolute Percentage Error (MAPE) between an actual and predictive data. The validation of error shows a value of 36.88% which is acceptable when the value close to zero. Thus, a small error indicates the high effectiveness performances in develop model prediction housing demand in Selangor. The finding helps Lembaga Perumahan dan Hartanah Selangor (LPHS) and other government agency to plan constructions for all housing categories in line with the demand.

Name dc:type.qualificationname

mphil

Level dc:type.qualificationlevel

masters

Grantor dc:publisher.institution

Universiti Tun Hussein Onn Malaysia

Year dc:date.issued

2018

Author dc:creator

• Mohamad Rawan, Nurfilzah

• HD Industries. Land use. Labor

Language dc:language

en