Long-term regional house prices cycles. A city-based index for Italy

2017 ◽  
Vol 10 (3) ◽  
pp. 303-330 ◽  
Author(s):  
Laura Gabrielli ◽  
Paloma Taltavull de La Paz ◽  
Armando Ortuño Padilla
Keyword(s):  
Time Series ◽  
Price Index ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
City Centre ◽  
House Price Index ◽  
Content Type ◽  
The Common

Purpose This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data. Design/methodology/approach This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals. Findings Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level. Research limitations/implications Data are measured as the average price in squared meters, and the resulting index is not quality controlled. Practical implications The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices. Originality/value This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

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.

scholarly journals Spatial–temporal neighbourhood-level house price index

2018 ◽  
Vol 11 (2) ◽  
pp. 386-411 ◽  
Author(s):  
Ibrahim Sipan ◽  
Abdul Hamid Mar Iman ◽  
Muhammad Najib Razali
Keyword(s):  
Design Methodology ◽  
Price Index ◽  
House Prices ◽  
House Price ◽  
House Price Index ◽  
Content Type ◽  
Gis Application ◽  
Statistical Application ◽  
Neighbourhood Level ◽  
Spatio Temporal

Purpose The purpose of this study is to develop a spatio-temporal neighbourhood-level house price index (STNL-HPI) incorporating a geographic information system (GIS) functionality that can be used to improve the house price indexation system. Design/methodology/approach By using the Malaysian house price index (MHPI) and application of geographically weighted regression (GWR), GIS-based analysis of STNL-HPI through an application called LHPI Viewer v.1.0.0, the stand-alone GIS-statistical application for STNL-HPI was successfully developed in this study. Findings The overall results have shown that the modelling and GIS application were able to help users understand the visual variation of house prices across a particular neighbourhood. Research limitations/implications This research was only able to acquire data from the federal government over the period 1999 to 2006 because of budget limitations. Data purchase was extremely costly. Because of financial constraints, data with lower levels of accuracy have been obtained from other sources. As a consequence, a major portion of data was mismatched because of the absence of a common parcel identifier, which also affected the comparison of this system to other comparable systems. Originality/value Neighbourhood-level HPI is needed for a better understanding of the local housing market.

scholarly journals The sources of house price change: identifying liquidity shocks to the housing market

2016 ◽  
Vol 9 (1) ◽  
pp. 98-120 ◽  
Author(s):  
Paloma Taltavull de La Paz ◽  
Michael White
Keyword(s):  
Monetary Policy ◽  
Housing Market ◽  
Error Correction ◽  
House Prices ◽  
Price Change ◽  
Housing Prices ◽  
House Price ◽  
Content Type ◽  
The Uk

Purpose The purpose of this paper is to examine the role of monetary liquidity in house price evolution through examining the Asset (housing) Inflation channel. It identifies the main channels of transmission affecting house prices from monetary supply channels to house price change, examining how the Asset Price channel transmits changes in M1 to housing prices in Spain and the UK. Design/methodology/approach The paper uses Vector Auto Regression (VAR) and Error Correction models to test the Asset Inflation channel in the UK and Spain from 1991 to 2013 in two steps. In the first step, the supply elasticity is estimated through the long-term relationship between house prices and stock supply. The second step estimates a Vector Error Correction (VEC) to explain house price dynamics conditioned on supply reactions. The latter is defined as a long-term inverse demand model where housing prices are controlled by fundamentals in each market. Models allow forecast testing using Choleski impulse responses methodology. Findings Several results are found. In the supply model, both countries show rapid convergence to equilibrium with a larger elasticity of supply in Spain than in the UK but with a short run effect of new supply on prices in the UK. Regarding the Asset Inflation Channel model, the paper finds evidence of the existence of a housing accelerator effect in Spain, but not in the UK where changes in liquidity fully impact house prices in one direction. Research limitations/implications Implications of findings are mainly to forecast the effects of Monetary Policy measures in different economies. Practical implications The model supports the evaluation of different impacts of monetary policy in territories. It shows that the same policy will have different impacts in different housing markets and therefore highlights the importance of examining each market separately to identify the appropriate policy interventions. Originality/value This is the first paper that estimates the impact of the Asset Inflation Channel on house prices that endogenises housing market conditions and compares effects and interrelationships in two different economies.

scholarly journals Liquidity, interest rates and house prices in the euro area: a DSGE analysis

2016 ◽  
Vol 9 (1) ◽  
pp. 4-25 ◽  
Author(s):  
Margarita Rubio ◽  
José A. Carrasco-Gallego
Keyword(s):  
Interest Rates ◽  
Monetary Union ◽  
Euro Area ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Dsge Model ◽  
Content Type ◽  
The Core ◽  
Dynamic Stochastic

Purpose This study aims to build a two-country monetary union dynamic stochastic general equilibrium (DSGE) model with housing to assess how different shocks contributed to the increase in housing prices and credit in the European Economic and Monetary Union. One of the countries is calibrated to represent the core group in the euro area, while the other one corresponds to the periphery. Design/methodology/approach In this paper, the authors explore how a liquidity shock (or a decrease in the interest rate) affects house prices and the real economy through the asset price and the collateral channel. Then, they analyze how a house price shock in the periphery and a technology shock in the core countries are transmitted to both economies. Findings The authors find that a combination of an increase in liquidity in the euro area coming from the common monetary policy, together with asymmetric house price and technology shocks, contributed to an increase in house prices in the euro area and a stronger credit growth in the peripheral economies. Originality/value This paper represents the theoretical counterpart to empirical studies that show, through macroeconometric models, the interrelation between liquidity and other shocks with house prices. Using a DSGE model with housing, the authors disentangle the mechanisms behind these empirical findings.

scholarly journals A dynamic model for housing price spillovers with an evidence from the US and the UK markets

2018 ◽  
Vol 2 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Alper Ozun ◽  
Hasan Murat Ertugrul ◽  
Yener Coskun
Keyword(s):  
New York ◽  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Housing Markets ◽  
Content Type ◽  
Real Estate Markets ◽  
Country Level ◽  
The Us

Purpose The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies. Design/methodology/approach The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method. Findings The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach. Research limitations/implications One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets. Practical implications The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City. Social implications The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice. Originality/value The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

High house prices may be global systemic risk

2016 ◽  
Keyword(s):  
Interest Rates ◽  
Household Income ◽  
House Prices ◽  
House Price ◽  
Content Type ◽  
Price Boom ◽  
Advanced Economies ◽  
The Us ◽  
Property Markets

Subject The rise in global house prices. Significance In the first quarter of 2015, the global house price index, aggregating prices in 52 countries, was at about the same level as in early 2007, according to IMF data. This recovery has occurred in a period of wage gains in most emerging markets (EMs), but little or no growth in household income across most advanced economies. Living costs excluding housing have stagnated and interest rates have been exceptionally low. Yet US interest rates are rising now and global prices are unlikely to keep falling beyond 2016, while many EMs have slumped into recession. As households are hit by more adverse trends, property markets and the related sectors will be affected. Impacts The EM house price boom will be curbed by slowing income growth and weaker economic prospects. High house-prices-to-household-income ratios and household debt might require the introduction of macroprudential tools. The US housing market will stay affordable compared to its long-term average and to Europe's.

Asymmetric sensitivities of house prices to housing fundamentals: evidence from UK regions

2019 ◽  
Vol 12 (3) ◽  
pp. 442-455 ◽  
Author(s):  
Huthaifa Alqaralleh
Keyword(s):  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Housing Affordability ◽  
Star Model ◽  
Content Type ◽  
Macroeconomic Variables ◽  
North West ◽  
Country Level ◽  
The North

Purpose This paper aims to examine asymmetries in the house price cycle and to understand the dynamic of housing prices, incorporating macroeconomic variables at regional and country level, namely, housing affordability, the unemployment rate, mortgage rate and inflation rate. Design/methodology/approach To highlight significant differences in the asymmetric patterns of house prices between regions, the STAR model is adopted. Findings The authors highlight significant differences in the asymmetric patterns of house prices between regions, in which some areas showed asymmetric response over the housing cycle; here the LSTAR model outperforms other models. In contrast, some regions (the South West and the North West) showed symmetric properties in the tails of the cycle; therefore, the ESTAR model was adopted in their case. Practical implications Being limited to a few fundamentals, this study opens an avenue for further research to investigate this dynamic using in addition such demand-supply factors as land supply, construction cost and loans made for housing. These findings can also be used to examine whether other models such as ARIMA, exponential smoothing or artificial neural networks can more accurately forecast housing prices. Originality/value The present paper aims to highlight housing affordability as a cause of asymmetric behaviour in house prices. Put differently, the authors seek to understand the dynamics of housing prices with other fundamentals incorporating macroeconomic variables in regions and country level data as a means of achieving a more concise result.

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