On non-negative equity guarantee calculations with macroeconomic variables related to house prices

2022 ◽  
Author(s):  
Alexandru Badescu ◽  
Enoch Quaye ◽  
Radu Tunaru
Keyword(s):  
House Prices ◽  
Macroeconomic Variables

Time-frequency linkages of international housing markets and macroeconomic drivers

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hardik Marfatia
Keyword(s):  
Housing Market ◽  
Time Domain ◽  
House Prices ◽  
Housing Markets ◽  
Income Growth ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Time Frequency ◽  
Direction Of Causation ◽  
Over Time

Purpose The studies on international housing markets have not modeled frequency domain and focused only on the time domain. The purpose of the present research is to fill this gap by using the state-of-the-art econometric technique of wavelets to understand how differences in the horizon of analysis across time impact international housing markets’ relationship with some of the key macroeconomic variables. The purpose is to also analyze the direction of causation in the relationships. Design/methodology/approach The author uses the novel time–frequency analysis of international housing markets’ linkages to the macroeconomic drivers. Unlike conventional approaches that do not distinguish between time and frequency domain, the author uses wavelets to study house prices’ relationship with its drivers in the time–frequency space. The novelty of the approach also allows gaining insights into the debates that deal with the direction of causation between house price changes and macroeconomic variables. Findings Results show that the relationship between house prices and key macroeconomic indicators varies significantly across countries, time, frequencies and the direction of causation. House prices are most related to interest rates at the higher frequencies (short-run) and per capita income growth at the lower frequencies (long-run). The role of industrial production and income growth has switched over time at lower frequencies, particularly, in Finland, France, Sweden and Japan. The stock market’s nexus with the housing market is significant mainly at high to medium frequencies around the recent financial crisis. Research limitations/implications The present research implies that in contrast to the existing approaches that are limited to the only time domain, the frequency considerations are equally, if not more, important. Practical implications Results show that interested researchers and analysts of international housing markets need to account for the both horizon and time under consideration. Because the factors that drive high-frequency movements in housing market are very different from low-frequency movements. Furthermore, these roles vary over time. Social implications The insights from the present study suggest policymakers interested in bringing social change in the housing markets need to account for the time–frequency dynamics found in this study. Originality/value The paper is novel on at least two dimensions. First, to the best of the author’s knowledge, this study is the first to propose the use of a time–frequency approach in modeling international housing market dynamics. Second, unlike present studies, it is the first to uncover the direction of causation between house prices and economic variables for each frequency at every point of time.

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.

scholarly journals MACROECONOMIC VARIABLES INFLUENCING HOUSING PRICES IN VILNIUS

2021 ◽  
Vol 0 (0) ◽  
pp. 1-11
Author(s):  
Alfredas Laurinavičius ◽  
Antanas Laurinavičius ◽  
Algimantas Laurinavičius
Keyword(s):  
Internal Migration ◽  
Inflation Rate ◽  
House Prices ◽  
Housing Prices ◽  
Gdp Per Capita ◽  
Macroeconomic Variables ◽  
Per Capita ◽  
The Way

The way macroeconomic variables such as unemployment/GDP per capita/inflation/wages/internal migration influenced housing prices (nominal house prices and housing rent prices) in Vilnius in 2006–2019 has been investigated in the research. Conditions under which different macroeconomic variables could influence housing prices were established in the research. Lower unemployment, higher GDP per capita and inflation rate were all related to higher nominal house prices in Vilnius. Higher GDP per capita, wages and internal migration were positively related to housing rent prices in Vilnius. Analyzed macroeconomic variables all together explained 88 percent of variance of nominal house prices in Vilnius over the period of 2006–2019 and 80 percent of variance of housing rent prices in Vilnius over the same period.

Turkish Housing Market Dynamics: An Estimated DSGE Model

2021 ◽  
Vol 15 (2) ◽  
pp. 238-267
Author(s):  
Mustafa Ozan Yıldırım ◽  
Mehmet İvrendi
Keyword(s):  
Monetary Policy ◽  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Policy Instrument ◽  
Dsge Model ◽  
Macroeconomic Variables ◽  
Dynamic Stochastic ◽  
Monetary Policy Reaction Function ◽  
The Impact

In this article, we investigate the underlying driving dynamics behind house price variations in Turkey by estimating a dynamic stochastic general equilibrium (DSGE) model in which the housing market and collateral constraints are included. The model also analyses the interaction between macroeconomic variables and the housing market by making policy simulations under different loan-to-value (LTV) ratios, which are used as a housing market-specific economic policy tool. The model is extended by including the traditional Taylor rule with house prices for representing monetary policy. Our findings show that house prices in Turkey are largely explained by housing preference shocks. Besides, we find that monetary policy shock plays a small role in determining the variables of the housing market in the short-term period. However, the magnitude of the impact of housing market shocks on the rest of the economy depends on the LTV ratios. The higher the LTV ratio, the higher are the effects of the government’s housing policy instrument for stabilising the housing market on real macroeconomic variables such as consumption and output in Turkey. Finally, our findings show that the fluctuations in house prices have not played a substantial role in the monetary policy reaction function of Turkey. JEL Codes: E32, E52, E44, E51, R31

scholarly journals Explaining Private Debt

2017 ◽  
Vol 8 (2) ◽  
pp. 153-176
Author(s):  
Lenno Uusküla
Keyword(s):  
Interest Rates ◽  
Inflation Rate ◽  
House Prices ◽  
House Price ◽  
Private Debt ◽  
Macroeconomic Variables ◽  
Income Ratio ◽  
Low Interest Rates ◽  
The Government ◽  
The Relationship

The paper examines the relationship between more than 30 macroeconomic variables and debt-to-GDP ratios for the household, non-financial corporation and aggregate debt in a panel of European Union countries. The GDP level and the ratio of house prices to income are found to be positively correlated with the debt-to-GDP ratio, whereas the real interest rate, the inflation rate, economic sentiment and the government debt level are negatively correlated with the debt-to-GDP ratio. Low interest rates and the house price-to-income ratio predict growth in the future debt-to-GDP ratio. Moreover, countries that have had a financial crisis have typically gone through a period of deleveraging afterwards.

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