The relationship between housing prices and inflation rate in Turkey

2019 ◽  
Vol 13 (3) ◽  
pp. 427-452 ◽  
Author(s):  
Özge Korkmaz
Keyword(s):  
Price Index ◽  
Inflation Rate ◽  
Housing Prices ◽  
Consumer Price Index ◽  
Housing Price ◽  
Human Beings ◽  
Monthly Basis ◽  
Data Set ◽  
Content Type ◽  
The Relationship

Purpose Human beings need shelter as the beginning of their existence. Same holds true for people who live in Turkey as it is a cultural and traditional reason to be the host and endeavor to buy a home even if one has to pay the debt for years. Another factor that is important for individuals and even for countries is the inflation rate. In this context, the purpose of this study is to investigate whether the 26 regions of Turkey are affected by the inflationary pressure, specifically in the housing price index (HPI). Design/methodology/approach For this purpose, data from 2010:01 to 2019:01 and the consumer price index (CPI), as well as HPI have been used. The causal relationship between the variables is analyzed by Konya Causality (2006) test. Findings The key results suggest that HPI causes inflationary pressures in some regions. Research limitations/implications The study has some limitations in terms of data set and scope. These are as follows: although there are many variables affecting housing prices, this study aims to investigate the causal link between inflation and housing prices. In addition, only the CPI and HPI variables were provided on a monthly basis in the 2010-2019 period for 26 regions due to the aim of making regional propositions in the investigation of this relationship. For these reasons, different macroeconomic variables could not be studied. Originality/value This study makes the following contribution to the literature. While the majority of existing literature investigates the relationship between housing prices and inflation from an empirical perspective for country, very few studies have been for the sub-regions and also these studies have focused on only some sub-regions. In other words, in the literature review, a study has observed that Turkey has to examine the relationship between the housing price and inflation variables for all sub-regions in particular. To overcome this deficiency in the literature, this study aims to investigate the relationship between housing price and inflation for 26 regions.

House price index (HPI) and Covid-19 pandemic shocks: evidence from Turkey and Kazakhstan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yeşim Aliefendioğlu ◽  
Harun Tanrivermis ◽  
Monsurat Ayojimi Salami
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Housing Prices ◽  
House Price ◽  
Monthly Basis ◽  
Data Set ◽  
House Price Index ◽  
Content Type ◽  
Asymmetric Pricing ◽  
Long Run

Purpose This paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan. Design/methodology/approach Monthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis. Findings The findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets. Research limitations/implications The main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices; however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study. Practical implications Despite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision. Originality/value This is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.

Impact of macroeconomic indicators on housing prices

2019 ◽  
Vol 12 (6) ◽  
pp. 1055-1071 ◽  
Author(s):  
Satish Mohan ◽  
Alan Hutson ◽  
Ian MacDonald ◽  
Chung Chun Lin
Keyword(s):  
Price Index ◽  
Time Series Data ◽  
Statistical Significance ◽  
Housing Prices ◽  
Consumer Price Index ◽  
New York State ◽  
Housing Price ◽  
Series Data ◽  
Content Type ◽  
Macroeconomic Indicators

Purpose This paper uses statistical analyses to quantify the effects of five major macroeconomic indicators, namely crude oil price, 30-year mortgage interest rate (IR), Consumer Price Index (CPI), Dow Jones Industrial Average (DJIA), and unemployment rate (UR), on housing prices over time. Design/methodology/approach Housing price is measured as housing price index (HPI) and is treated as a variable affecting itself. Actual housing sale prices in the Town of Amherst, New York State, USA, 1999-2008, and time-series data of the macroeconomic indicators, 2000-2017, were used in a vector autoregression statistical model to examine the data that show the greatest statistical significance and exert maximum quantitative effects of macroeconomic indicators on housing prices. Findings The analyses concluded that the 30-year IR and HPI have statistically significant effects on housing prices. IR has the highest effect, contributing 5.0 per cent of variance in the first month to 8.5 per cent in the twelfth. The UR has the next greatest influence followed by DJIA and CPI. The disturbance from HPI itself causes the greatest variability in future prices: up to 92.7 per cent in variance 1 month ahead and approximately 74.5 per cent 12 months ahead. This result indicates that current changes in house prices heavily influence people’s expectation of future prices. The total effect of the error variance of the macroeconomic indicators ranged from 7.3 per cent in the first month to 25.5 per cent in the twelfth. Originality/value The conclusions in this paper, along with related tables and figures, will be useful to the housing and real estate communities in planning their business for the next years.

Property investor decisions using income and rental ratio signals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billie Ann Brotman
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Discount Rate ◽  
Price Index ◽  
Housing Prices ◽  
Housing Price ◽  
Consumer Sentiment ◽  
Content Type ◽  
Income Ratio ◽  
Real Estate Prices

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

Driving forces for the US residential housing price: a predictive analysis

2019 ◽  
Vol 9 (4) ◽  
pp. 515-529
Author(s):  
Amirhosein Jafari ◽  
Reza Akhavian
Keyword(s):  
Real Estate ◽  
Predictive Models ◽  
Driving Forces ◽  
Housing Prices ◽  
Housing Price ◽  
Data Set ◽  
Content Type ◽  
The Usa ◽  
Decision Making Processes ◽  
The Impact

Purpose The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes. Design/methodology/approach A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity. Findings Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics. Research limitations/implications An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation. Practical implications The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors. Social implications Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices. Originality/value Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.

Factors influencing the price of housing in Indonesia

2015 ◽  
Vol 8 (2) ◽  
pp. 169-188 ◽  
Author(s):  
Raden Aswin Rahadi ◽  
Sudarso Kaderi Wiryono ◽  
Deddy Priatmodjo Koesrindartoto ◽  
Indra Budiman Syamwil
Keyword(s):  
Factor Analysis ◽  
Real Estate ◽  
Design Methodology ◽  
Housing Prices ◽  
Housing Price ◽  
Correlation Study ◽  
Metropolitan Region ◽  
Location Factors ◽  
Content Type ◽  
The Relationship

Purpose – This study aims to address the factors or attributes that would influence the price of residential products in Jakarta Metropolitan Region. Design/methodology/approach – In total, 202 respondents from all across Jakarta Metropolitan Region participated in the questionnaire for this study. Demographic questions are categorized into age, gender and preferences for real estate locations. The questionnaire was made based on the author’s previous studies. Of the total respondents, 127 were males and 75 were females with age ranging from 18 to 56 years old. For data analysis, the authors utilized factor analysis, Cronbach’ α test and analysis of correlation to reach the conclusion of this study. Findings – The findings suggested that from the initial three factors groups, there are five new groups that emerge as influencing factors for housing prices. Cronbach’ α score were verified (α = 0.906). Correlation study result suggested that the initial three factors groups produce a significant correlation between each of them, except for the factor of “overall location” and “located near family.” After factor analysis, the research results show that there are two new additional groups of factors that emerge as influences to housing prices. There are significant scores of differences between gender and real estate location preference toward the groups of factors. Research limitations/implications – This study shows how physical qualities, concept and location factors influence the housing price perception of their consumers. The result shows to be relatively reliable and valid. Originality/value – The study is the first to analyze the relationship between the factors for preferences on residential products and housing price in Indonesia. This paper is also intended to be the first to pioneer the study on factors of preferences on residential products in Indonesia. The findings will be useful to develop pricing models for housing product in Indonesia.

Housing price volatility: uncertainty, an asymmetric econometric analysis - some European country experiences

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudeshna Ghosh
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Housing Price ◽  
Price Volatility ◽  
Wald Test ◽  
Policy Uncertainty ◽  
Content Type ◽  
Distributed Lag ◽  
Long Run ◽  
Asymmetric Impact

Purpose The purpose of this paper is to examine the asymmetric impact of economic policy uncertainty (EPU) on the volatility of the housing price index (RP) based on quarterly observations from major European countries, namely, France, Germany, Sweden, Greece Italy and the UK. Design/methodology/approach The nonlinear autoregressive distributed lag model method is used to investigate the asymmetric impact of EPU on RP. In addition to considering EPU as the explanatory variable, industrial production (IP) (as a proxy for economic growth), interest rate (I), inflationary tendency (Consumer Price Index) and share prices (S) are included as major control variables. The period of the observations runs from 1996Q1 to 2019Q1. Findings The Wald test confirms the long-run asymmetric relationship for all countries. The alternative specification of the data sets reconfirms the asymmetric impact on RP in the long run, thereby verifying the robustness of the study. Research limitations/implications The study has implications for investors seeking to incorporate housing price behaviour within their portfolio structure. The analysis and findings are constrained by the availability of data. Originality/value This is one of the few studies on housing price dynamics related to the major economies of the European region that explore asymmetries. Additionally, it is the first to explore the asymmetry dynamics using the EPU variable.

scholarly journals The relationship between effective interest rates and the consumer price index CPI as an inflation measure: evidence from Jordan

2019 ◽  
Vol 9 (2) ◽  
pp. 65-71
Author(s):  
Osama Samih Shaban ◽  
Mohammad Al-Attar ◽  
Nafez Nimer Ali
Keyword(s):  
Correlation Analysis ◽  
Interest Rates ◽  
Price Index ◽  
Inflation Rate ◽  
Pearson Correlation ◽  
Consumer Price Index ◽  
Negative Relationship ◽  
Real Relationship ◽  
The Real ◽  
The Relationship

The aim of this research is to figure out the type of relationship between the effective interest rates and the consumer price index rate CP and to determine the real relationship between them. In order to achieve the desired objectives of the research, we calculated the rate of inflation through the change in the consumer price index CPI, for the period 2010-2018. A Pearson Correlation is also conducted between the CPI rates and effective interest rates for the same period. The outcomes of CPI calculations show that the CPI average for the year 2018 reached 124.66 points, indicating a 5.33% difference from the same period of 2017, and this difference is referred to as the inflation rate, also, the outcomes of the correlation analysis conducted refers to a negative relationship between the CPI rates and the effective interest rates.

Customer satisfaction, value for money and repurchase intent in the context of system delivery projects: a longitudinal study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Matti Haverila ◽  
Jenny Carita Twyford
Keyword(s):  
Project Management ◽  
Customer Satisfaction ◽  
Effect Sizes ◽  
Relational Exchange ◽  
Value For Money ◽  
Data Set ◽  
Content Type ◽  
Repurchase Intent ◽  
The Relationship ◽  
Relational Exchange Theory

PurposeDrawing upon the relational exchange theory, the longitudinal relationship between various stages of project management customer satisfaction, value for money and repurchase intent are examined.Design/methodology/approachUsing a survey questionnaire, data were gathered over four consecutive quarters (N = 2,537). The statistical methods included exploratory factor analysis, confirmatory composite analysis (CCA) and partial least squares structural equation modeling (PLS-SEM).FindingsProject management was perceived as a three-dimensional construct (proposal, installation, commissioning/start-up). There was a significant longitudinal relationship between project stages and satisfaction in the complete data set. The results varied on the quarterly basis. The relationship customer satisfaction/repurchase intent was significant in the whole data set and during all quarters. This was the case for the relationships between value for money and customer satisfaction and between value for money and repurchase intent. The effect sizes were small between project management stages and customer satisfaction, small to medium for the value for money construct and large for the customer satisfaction construct.Originality/valueAn important implication is the significant relationship between the stages of project management and satisfaction. However, the effect sizes were small, however. The importance of the effect size in comparison to the significance of the relationships is highlighted especially when the sample size is large. The paper also confirms the linear relationship between satisfaction and repurchase intent. The nature of the relationship between customer satisfaction and loyalty is based on a moderate exchange relationship in the relational exchange continuum. The study contributes to the relational exchange theory in the context of project management.

Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijat Arun Abhyankar ◽  
Harish Kumar Singla
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Multivariate Regression ◽  
Housing Prices ◽  
Predictive Performance ◽  
General Regression Neural Network ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Content Type ◽  
General Regression

Purpose The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.” Design/methodology/approach Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016). Findings While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%). Research limitations/implications The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices. Practical implications The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence. Originality/value To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

The impact of organizational position level and cultural flow direction on the relationship between cultural intelligence and expatriate cross-border adaptation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ying Zhang ◽  
Yuran Li ◽  
Mark Frost ◽  
Shiyu Rong ◽  
Rong Jiang ◽  
...  
Keyword(s):  
Cultural Intelligence ◽  
Flow Direction ◽  
Power Distance ◽  
Cultural Adjustment ◽  
Data Set ◽  
Content Type ◽  
Cross Border ◽  
Organizational Position ◽  
The Impact ◽  
The Relationship

PurposeThis paper aims to examine the critical role played by cultural flow in fostering successful expatriate cross-border transitions.Design/methodology/approachThe authors develop and test a model on the interplay among cultural intelligence, organizational position level, cultural flow direction and expatriate adaptation, using a data set of 387 expatriate on cross-border transitions along the Belt & Road area.FindingsThe authors find that both organizational position level and cultural flow moderate the relationship between cultural intelligence and expatriate adaptation, whereby the relationship is contingent on the interaction of organizational position status and assignment directions between high power distance and low power distance host environments.Originality/valuePrevious research has shown that higher levels of cultural intelligence are positively related to better expatriate adaptation. However, there is a lack of research on the effect of position difference and cultural flow on such relationship. Our study is among the first to examine how the interaction between cultural flow and organizational position level influences the cultural intelligence (CI) and cultural adjustment relationship in cross-cultural transitions.

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