Revisiting the nexus between house pricing and money demand: Power spectrum and wavelet coherence based approach

Ma, Qingsong; Khan, Zeeshan; Chen, Fuzhong; Murshed, Muntasir; Siqun, Yang; Kırıkkaleli, Derviş

doi:10.1016/j.qref.2021.03.001

Cited by 12 publications

(5 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results validate the hypothesis that takes place in the research objectives as market variables have much more effect on housing prices; the COVID-19 pandemic is highly effective on housing prices; there is no difference between the factors affecting RPPI, NRPPI and ORPPI. The results obtained from the QR analysis are consistent with the results of the current studies (Yılmazkuday, 2020; Aliefendioğlu et al , 2021; Ma et al , 2021) and pre-expectations of the authors. However, there are some other studies with which the results are inconsistent (Akseki and Türkcan, 2016; Karadağ and Salihoğlu, 2020; Korkmaz, 2020).…”

Section: Empirical Analysissupporting

confidence: 92%

“…While the literature focuses on mainly housing demand (Follain and Jimenez, 1985;Zabel, 2004;Arslan et al, 2013;Chow and Niu, 2015;Hatipo glu and Tanrıvermis , 2017;Öztürk et al, 2018;Arestis and Jia, 2019;Pontiggia and Sivitanides, 2020) and housing supply (Follain, 1979;Stover, 1986;Öztürk and Fitöz, 2009;Davidoff, 2013;Chow and Niu, 2015;Ciarlone, 2015;Öztürk et al, 2018;Pontiggia and Sivitanides, 2020), housing prices have also been coming to the attention of researchers recently (Mohan et al, 2019;Korkmaz, 2020;Pandey and Jessica, 2020;Yılmazkuday, 2020;Aliefendio glu et al, 2021;Ma et al, 2021). Hence, the present literature involves studies on housing prices examining the impacts of various variables on housing prices.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Housing prices in emerging countries during COVID-19: evidence from Turkey

Kartal

Depren

2021

IJHMA

View full text Add to dashboard Cite

Purpose By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks. Design/methodology/approach A quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness. Findings Predictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables; the pandemic and rent have the highest effect on the indices; The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles; the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles; the results for RPPI, NRPPI and ORPPI are consistent and robust. Research limitations/implications The results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study. Practical implications The COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables. Social implications Emerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets. Originality/value The QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens.

show abstract

Section: Empirical Analysissupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Housing prices in emerging countries during COVID-19: evidence from Turkey

Kartal

Depren

2021

IJHMA

View full text Add to dashboard Cite

show abstract

“…The empirical findings obtained from the Wavelet method, which is also used by Ma et al (2021), show that there is a positive relationship between inflation and housing prices in the short and long run, in line with the economic literature (İslamoğlu & Şabanoğlu, 2019;Walentin, 2014;Paksoy et al, 2014;Akkay 2021;Malmendier & Wellsjo, 2023). According to the literature, it's critical to comprehend the causes, consequences, and dynamics of housing inflation as well as previous inflation experiences in order to predict how buyers and sellers will act in the housing market.…”

supporting

confidence: 81%

“…For that reason, we use the log of all three variables. Determining the relationship with the wavelet transform coherence (WTC) model was used by Ma et al (2021), and we are going to do analysis with the same model by using the R-language program. The main hypotheses of the study are as follows: H 0 : changes in CPI have no impact on Turkish house prices, H 1 : changes in CPI have an impact on Turkish house prices.…”

mentioning

confidence: 99%

A 10-Year Analysis of Housing Prices and The Influence of Economic Factors in Turkey

Muddasir,

Dondaş

2023

Financial Internet Quarterly

View full text Add to dashboard Cite

In Turkey, the housing market is affected by various factors, and housing prices are shaped according to current conditions. In this study, the relationship between the housing price index (HPI) and economic variables (inflation and exchange rate) in Turkey was investigated. We used a 10-year time period for this study, from January-2010 to December-2019, with monthly data frequency. For our research, we employ the Wavelet Coherence Transformation (WCT) method. The results show that there is a positive relationship between inflation and housing prices in the short and long run. In addition, a continuous rise in inflation led to an increase in housing prices all over the period from 2010 to 2019. The findings of this study can aid in achieving the goal of the research by offering evidence-based perceptions of how housing prices and different economic variables are related. Housing costs in Turkey increased as a result of the substantial likelihood that the Turkish lira would weaken. The expansion of global inflation is a further anticipated factor contributing to the rise in housing costs in Turkey. This study can also be used by investors to help them decide whether to engage in the Turkish real estate industry.

show abstract

“…Despite its importance, the House Price Index has not been sufficiently explored by researchers in this century [5][6][7]. The overall home value is influenced by a lot of factors, including but not limited to physical states, concepts, and locations.…”

Section: Introductionmentioning

confidence: 99%

House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection

Basysyar¹,

Dwilestari²

2022

ATAIML

View full text Add to dashboard Cite

In many real-world applications, it is more realistic to predict a price range than to forecast a single value. When the goal is to identify a range of prices, price prediction becomes a classification problem. The House Price Index is a typical instrument for estimating house price discrepancies. This repeat sale index analyzes the mean price variation in repeat sales or refinancing of the same assets. Since it depends on all transactions, the House Price Index is poor at projecting the price of a single house. To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net regression, with the aid of machine learning with feature selection. The proposed prediction model for house prices was evaluated on a machine learning housing dataset, which covers 1,460 records and 81 features. By comparing the predicted and actual prices, it was learned that our model outputted an acceptable, expected values compared to the actual values. The error margin to actual values was very small. The comparison shows that our model is satisfactory in predicting house prices.

show abstract

Revisiting the nexus between house pricing and money demand: Power spectrum and wavelet coherence based approach

Cited by 12 publications

References 55 publications

Housing prices in emerging countries during COVID-19: evidence from Turkey

Housing prices in emerging countries during COVID-19: evidence from Turkey

A 10-Year Analysis of Housing Prices and The Influence of Economic Factors in Turkey

House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection

Contact Info

Product

Resources

About