scholarly journals Examining the Effect of Squatter Settlements in the Evolution of Spatial Fragmentation in the Housing Market of the City of Buenos Aires by Using Geographical Weighted Regression

2021 ◽  
Vol 10 (6) ◽  
pp. 359
Author(s):  
A. Federico Ogas-Mendez ◽  
Yuzuru Isoda
Keyword(s):  
Housing Market ◽  
Low Income ◽  
Buenos Aires ◽  
House Prices ◽  
House Price ◽  
Spatiotemporal Analysis ◽  
Weighted Regression ◽  
Squatter Settlements ◽  
Squatter Settlement ◽  
Low Income Households

The spatial fragmentation in the housing market and the growth of squatter settlements are characteristic for the metropolitan areas in developing countries. Over the years, in large cities, these phenomena have been promoting an increase in the spatial concentration of poverty. Therefore, this study examined the relationship between the squatter settlement growth and spatial fragmentation in the housing market of Buenos Aires. By performing a spatiotemporal analysis using geographically weighted regression in the house prices for the years 2001, 2010, and 2018, the results showed that while squatter settlements had a strong negative effect on house prices, the affected areas shifted over time. Our findings indicate that it is not the growth of the squatter settlement that causes spatial fragmentation, but rather the widening income disparities and further segregation of low-income households. However, squatter settlements determined the spatial demarcation of fragmented housing market by attracting low-income households to surrounding low house price areas.

scholarly journals Housing prices in peninsular Malaysia: supported by income, foreign inflow or speculation?

2019 ◽  
Vol 12 (3) ◽  
pp. 424-441 ◽  
Author(s):  
Wang Li Wong ◽  
Chin Lee ◽  
Seow Shin Koong
Keyword(s):  
Housing Market ◽  
Low Income ◽  
House Prices ◽  
Correction Term ◽  
Housing Prices ◽  
House Price ◽  
Peninsular Malaysia ◽  
Financial Asset ◽  
Content Type ◽  
The Impact

Purpose This paper is motivated by a concern about the ability of the average Malaysian income to catch up with the rapidly increasing house prices in Peninsular Malaysia. Financial innovation in financial system now regards houses as a financial asset and speculation vehicle. Therefore, a house purchase is made to acquire not merely a necessity but also a financial asset which can generate future returns. Given the problems in the housing market, this paper aims to examine the determinants of house prices in Malaysia, including those such as income, population, foreign inflow and speculation. Design/methodology/approach This study adopts panel data analyses, namely, the fixed effect model (FEM) and the pooled mean group (PMG), and uses data at state level in quarterly frequency, spanning from 2005Q1 to 2013Q4. Findings Based on the results of FEM, these determinants influence house prices significantly. Moreover, the PMG results suggest that there is convergence in the model, which are indicated by the significant and negative sign of the error correction term. In conclusion, the rapidly increasing house price is not caused by speculation activities in the housing market. More precisely, Malaysian income is capable of catching up with the increasing house prices. Practical implications As income remains to be one of the major drivers in influencing Malaysian house price, Malaysian Government shall continue the policies of supply low cost houses to the low-income groups and My First Home Scheme (SRP) by offering less stringent rules in applying house loan for the first-time house buyers. Originality/value This study used the actual data of foreign housing purchase obtained from Malaysia Valuation and Property Services Department to represent foreign inflow; therefore, the results will reflect the impact of foreign inflow in a better manner.

Housing market will be an election issue in Canada

2021 ◽  
Keyword(s):  
Housing Market ◽  
Affordable Housing ◽  
Low Income ◽  
Home Ownership ◽  
House Prices ◽  
Housing Demand ◽  
Credit Crunch ◽  
Content Type ◽  
Low Income Households

Significance A collapse in the housing market, which would hit indebted Canadians hard and contribute to a credit crunch that impacts the wider economy, remains unlikely but record house prices and the lack of affordable homes will be issues in the upcoming election. Impacts Consistently high house prices may see young and low-income households give up the idea of home ownership altogether. The prolonged failure of government to ensure more affordable housing will mean widening disparities between owners and renters. Housing demand in smaller markets around major cities, boosted by the pandemic, will cool with a return to office working.

scholarly journals The role and significance of planning in the determination of house prices in Australia: Recent policy debates

2021 ◽  
pp. 0308518X2198894
Author(s):  
Peter Phibbs ◽  
Nicole Gurran
Keyword(s):  
Land Use ◽  
Housing Market ◽  
Land Use Planning ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Evidence Base ◽  
Urban Housing ◽  
Price Inflation ◽  
Depth Analysis

On the world stage, Australian cities have been punching above their weight in global indexes of housing prices, sparking heated debates about the causes of and remedies for, sustained house price inflation. This paper examines the evidence base underpinning such debates, and the policy claims made by key commentators and stakeholders. With reference to the wider context of Australia’s housing market over a 20 year period, as well as an in depth analysis of a research paper by Australia’s central Reserve Bank, we show how economic theories commonly position land use planning as a primary driver of new supply constraints but overlook other explanations for housing market behavior. In doing so, we offer an alternative understanding of urban housing markets and land use planning interventions as a basis for more effective policy intervention in Australian and other world cities.

Rent or buy, what are the odds? Analysing the price-to-rent ratio for housing types within the Northern Ireland housing market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daniel Lo ◽  
Michael James McCord ◽  
John McCord ◽  
Peadar Thomas Davis ◽  
Martin Haran
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Main Property ◽  
Property Market ◽  
Important Indicator ◽  
Content Type ◽  
Rental Market ◽  
Price Series ◽  
The Relationship

Purpose The price-to-rent ratio is often regarded as an important indicator for measuring housing market imbalance and inefficiency. A central question is the extent to which house prices and rents form part of the same market and thus whether they respond similarly to parallel stimulus. If they are close proxies dynamically, then this provides valuable market intelligence, particularly where causal relationships are evident. Therefore, this paper aims to examine the relationship between market and rental pricing to uncover the price switching dynamics of residential real estate property types and whether the deviation between market rents and prices are integrated over both the long- and short-term. Design/methodology/approach This paper uses cointegration, Wald exogeneity tests and Granger causality models to determine the existence, if any, of cointegration and lead-lag relationships between prices and rents within the Belfast property market, as well as the price-to-rent ratios amongst its five main property sub-markets over the time period M4, 2014 to M12 2018. Findings The findings provide some novel insights in relation to the pricing dynamics within Belfast. Housing and rental prices are cointegrated suggesting that they tend to move in tandem in the long run. It is further evident that in the short-run, the price series Granger-causes that of rents inferring that sales price information unidirectionally diffuse to the rental market. Further, the findings on price-to-rent ratios reveal that the detached sector appears to Granger-cause those of other property types except apartments in both the short- and long-term, suggesting possible spill-over of pricing signals from the top-end to the lower strata of the market. Originality/value The importance of understanding the relationship between house prices and rental market performance has gathered momentum. Although the house price-rent ratio is widely used as an indicator of over and undervaluation in the housing market, surprisingly little is known about the theoretical relationship between the price-rent ratio across property types and their respective inter-relationships.

A look into German housing markets: A bubble call?

2018 ◽  
Vol 21 (4) ◽  
pp. 289-301
Author(s):  
Jan R. Kim ◽  
Gieyoung Lim
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Housing Markets ◽  
Present Value ◽  
Present Value Model ◽  
Price Bubbles ◽  
Steep Rise ◽  
Value Model ◽  
German Housing Market

The steep rise in German house prices in recent years raises the question of whether a speculative bubble has already emerged. Using a modified present-value model, we estimate the size of speculative house price bubbles in the German housing market. We do not find evidence for positive bubble accumulation in recent years, and interpret the current bullish run as reflecting the correction of house prices that have been undervalued for more than 10 years. With house prices close to their fair values as of 2018:Q1, our answer to the question is, ‘Not yet, but it is likely soon’.

scholarly journals Measuring heterogeneity of house price developments in Hungary, 1990–2016

2018 ◽  
Vol 68 (3) ◽  
pp. 377-414
Author(s):  
Ádám Banai ◽  
Nikolett Vágó ◽  
Sándor Winkler
Keyword(s):  
Time Series ◽  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Price Indices ◽  
Regional Heterogeneity ◽  
Economic Cycles ◽  
House Price Indices ◽  
Long Time ◽  
Index Family

This study presents the detailed methodology of generating house price indices for the Hungarian market. The index family is an expansion of the Hungarian housing market statistics in several regards. The nationwide index is derived from a database starting from 1990, and thus the national index is regarded as the longest in comparison to the house price indices available so far. The long time series allow us to observe and compare the real levels of house prices across economic cycles. Another important innovation of this index family is its ability to capture house developments by regions and settlement types, which sheds light on the strong regional heterogeneity underlying the Hungarian housing market.

Regional housing price dependency in the UK: A dynamic network approach

Urban Studies ◽  
2020 ◽  
pp. 004209802094348
Author(s):  
Dayong Zhang ◽  
Qiang Ji ◽  
Wan-Li Zhao ◽  
Nicholas J Horsewood
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Housing Price ◽  
Dynamic Network ◽  
Network Approach ◽  
Market Interactions ◽  
Partial Correlations ◽  
Regional Housing Market ◽  
The Uk

The cross-regional dependency in the UK housing market is analysed using regional house price indices. In this article, a network approach based on partial correlations is proposed, along with rolling-window analysis to consider potential time-varying dependency. The results show that house prices in the outer South East region have the strongest influence on regional housing market interactions in the UK. This influence is stronger when the markets are highly interconnected, whereas the house prices in London have the strongest influence when the UK regional housing markets are relatively less connected.

scholarly journals Club convergence of house prices: Evidence from China’s ten key cities

2015 ◽  
Vol 29 (24) ◽  
pp. 1550181 ◽  
Author(s):  
Hao Meng ◽  
Wen-Jie Xie ◽  
Wei-Xing Zhou
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Economic Recession ◽  
Housing Markets ◽  
Extensive Study ◽  
Ripple Effect ◽  
Chinese Stock Market ◽  
Price Growth ◽  
Chinese Housing Market

The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a marked fall during the global financial tsunami and China’s economy has also slowed down by about 2%–3% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than 10 years. However, the structure and dynamics of the Chinese housing market are less studied. Here, we perform an extensive study of the Chinese housing market by analyzing 10 representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5% of the house price growth, indicating very high systemic risk in the Chinese housing market. The 10 key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices among different cities.

Assessing Dutch housing cycle and near-term market prospects

2020 ◽  
Vol 13 (2) ◽  
pp. 257-270
Author(s):  
Arvydas Jadevicius ◽  
Peter van Gool
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Future Market ◽  
Price Inflation ◽  
Content Type ◽  
Dutch Housing ◽  
The Usa ◽  
Near Term ◽  
The Uk

Purpose This study is a practice undertaking examining three main concerns that currently dominate Dutch housing market debate: how long is the cycle, will the current house price inflation continue and is housing market in a bubble. With national house prices reaching record highs across all major cities, future market prospects became a topic of significant debate among policymakers, investors and the populace. Design/methodology/approach A triangulation of well-established academic methods is used to perform investigation. The models include Hodrick-Prescott (HP) filter, volatility autoregressive conditional heteroskedasticity (ARCH approximation) and right tail augmented Dickey–Fuller (Rtadf) test (bubble screening technique). Findings Interestingly, over the years from 1985 to 2019 research period, filtering extracts only one Dutch national housing cycle. This is a somewhat distinct characteristic compared to other advanced Western economies (inter alia the UK and the USA) where markets tend to experience 8- to 10-year gyrations. Volatility and Rtadf test suggest that current house prices in most Dutch cities are in excess of historical averages and statistical thresholds. House price levels in Almere, Amsterdam, The Hague, Groningen, Rotterdam and Utrecht are of particular concern. Originality/value Retail investors should therefore be cautious as they are entering the market at the time of elevated housing values. For institutional investors, those investing in long-term, housing in key Dutch metropolitan areas, even if values decline, is still an attractive investment conduit.

scholarly journals Augmenting Geostatistics with Matrix Factorization: A Case Study for House Price Estimation

2020 ◽  
Vol 9 (5) ◽  
pp. 288
Author(s):  
Aisha Sikder ◽  
Andreas Züfle
Keyword(s):  
Matrix Factorization ◽  
Geographically Weighted Regression ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Weighted Regression ◽  
Regression Kriging ◽  
Truncated Svd ◽  
Latent Features ◽  
Kriging Approach

Singular value decomposition (SVD) is ubiquitously used in recommendation systems to estimate and predict values based on latent features obtained through matrix factorization. But, oblivious of location information, SVD has limitations in predicting variables that have strong spatial autocorrelation, such as housing prices which strongly depend on spatial properties such as the neighborhood and school districts. In this work, we build an algorithm that integrates the latent feature learning capabilities of truncated SVD with kriging, which is called SVD-Regression Kriging (SVD-RK). In doing so, we address the problem of modeling and predicting spatially autocorrelated data for recommender engines using real estate housing prices by integrating spatial statistics. We also show that SVD-RK outperforms purely latent features based solutions as well as purely spatial approaches like Geographically Weighted Regression (GWR). Our proposed algorithm, SVD-RK, integrates the results of truncated SVD as an independent variable into a regression kriging approach. We show experimentally, that latent house price patterns learned using SVD are able to improve house price predictions of ordinary kriging in areas where house prices fluctuate locally. For areas where house prices are strongly spatially autocorrelated, evident by a house pricing variogram showing that the data can be mostly explained by spatial information only, we propose to feed the results of SVD into a geographically weighted regression model to outperform the orginary kriging approach.

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