The analysis of the determinants of housing prices

Ali

et al. 2022

IJHMA

Purpose This study aims to explore empirically the impact of various factors/determinants on housing prices at the country level as well as in Lahore, the most populous metropolitan city of the most populous province Punjab, Pakistan. Design/methodology/approach This study uses monthly data ranging from 2013M1 to 2020M1 on variables used in the study. Based on the stationarity results, the method of robust least square is used as an estimation technique. The validity of initial results is also authenticated by canonical cointegration regression. Findings The empirical result reveals that all included variables significantly affect housing prices both at country level as well as in Lahore. This study found negative impact of regressor age, real exchange rate and urbanization on housing prices, whereas the positive impact of gross domestic product (GDP) per capita, foreign remittances, broad money and real interest rate on housing prices in the case of Pakistan was found. On the other hand, results unveiled the negative impact of regressor age (proportion of population aged between 15 and 64), real exchange rate and urbanization on housing prices, whereas the positive impact of GDP per capita, foreign remittances, broad money and real interest rate on housing prices in Lahore metropolitan city was unveiled. Originality/value Based on the extant literature survey, this is a more holistic study of its kind that uncovers the macroeconomic determinants by considering the demand side, supply side and demographic factors of escalated housing prices in Pakistan, so that proper policies can be adopted to keep the housing sector stable. Empirical findings are helpful to acquire an enhanced understanding of how the housing price is determined and form a base for government to tackle the housing affordability problem.

Section: Literature Reviewmentioning

confidence: 99%

“…GDP per capita is one of the main determinants of housing prices. GDP per capita increase leads to an increase in housing demand like other commodities and hence housing prices are increased and vice versa and has been used in the studies by Cohen and Karpaviciute (2017) and Kakar and Kakar (2011) .…”

Section: Empirical Modelmentioning

confidence: 99%

Factors determining housing prices: empirical evidence from a developing country’s Pakistan

Ali

et al. 2022

IJHMA

“…For example, Cesa-Bianchi et al (2015), Tsatsaronis and Zhu (2004), Glindro et al (2008) and Hofmann (2003) used several financial and macroeconomic-inclined variables in examining the drivers for housing price. Numerous studies have been done on unveiling the macroeconomics and financial determinants of housing price in the isolation form for specific countries or city level [5]: Cyprus (Alola, 2020), the USA (Mohan et al , 2019), London, the United Kingdom (Sivitanides, 2018), the United Kingdom (Holly and Jones, 1997), Lithuania (Gasparėnienė et al , 2016; Cohen and Karpavičiūtė, 2017), Greece (Panagiotidis and Printzis, 2016; Katrakilidis and Trachanas, 2012; Brissimis and Vlassopoulos, 2008), Namibia (Kaulihowa and Kamati, 2019), Norway (Anundsen and Jansen, 2013) and China (Wang and Zhang, 2014; Deng et al , 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Residential property market in Malaysia: an analysis of price drivers and co-movements

Thaker

Ariff

Subramaniam

2020

PurposeThe purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.Design/methodology/approachThis study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.FindingsThe empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.Practical implicationsThis study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.Originality/valueThe findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

“…Cohen and Karpavičiūtė (2017) analyzed the data set from 2001 to 2014 and identified that approximately 99 per cent of the changes in the real estate prices in Lithuania are defined by determinants such as GDP, unemployment and introduction of macroprudential policy. Kok et al (2018) investigated macroeconomic factors influencing property prices in Malaysia and concluded that 60 per cent of changes in real estate market are explained by real GDP.…”

Section: Literature Reviewmentioning

confidence: 99%

Determinants of housing prices in Baku: empirical analyses

Aliyev

Amiraslanova

Bakirova

et al. 2019

IJHMA

Purpose This paper aims to reveal major factors affecting housing prices (flats and houses) in Baku, the capital of Azerbaijan Republic. Design/methodology/approach Based on cross-sectional data set of 497 flats and 443 houses, polynomial regression models are estimated for flats and houses separately. Regression models are estimated by using ordinary least squares. Findings Location, largeness, repair level and existence of bill of sale are major price determinants for flats. For houses, number of rooms also matters. Findings reveals that houses are land intensive (more floors, less land area) toward city center, and vice versa. Price difference due to existence of bill of sale diminishes significantly toward the surrounding areas. Research limitations/implications The data set represents view of sellers and does not take into consideration price bargaining in time of sale; probability of information asymmetries exists which not could accounted for, and urgency of sale is not considered. Practical implications Estimation results can be used for housing valuation by real estate market participants and investors. Social implications Research findings reveal importance of bill of sale as a major price determinant and expected to attract policymakers’ attention to solve such a big social problem. Additionally, models can be based for price estimations in Baku housing market. Originality/value The study contributes to the literature by empirically analyzing housing market in Baku, Azerbaijan. Research produces new practically valuable findings.