scholarly journals International Real Estate Review

2020 ◽  
Vol 23 (2) ◽  
pp. 267-308
Author(s):  
Are Oust ◽  
◽  
Ole Martin Eidjord ◽  
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Search Term ◽  
Housing Bubble ◽  
Search Terms ◽  
Search Volume ◽  
Google Search ◽  
The U.S

The aim of this paper is to test whether Google search volume indices can be used to predict house prices and identify bubbles in the housing market. We analyze the data that pertain to the 2006?2007 U.S. housing bubble, taking advantage of the heterogeneous house price development in both bubble and non-bubble states in the U.S. Using 204 housing-related keywords, we test both single search terms and indices that comprise search term sets to see whether they can be used as housing bubble indicators. We find that several keywords perform very well as bubble indicators. Among all of the keywords and indices tested, the Google search volume for ¡§Housing Bubble¡¨ and ¡§Real Estate Agent¡¨, and a constructed index that contains the twelve best-performing search terms score the highest at both detecting bubbles and not erroneously detecting non-bubble states as bubbles. A new housing bubble indicator may help households, investors, and policy makers receive advanced warning about future housing bubbles. Moreover, we show that the Google search outperforms the well-established consumer confidence index in the U.S. as a leading indicator of the housing market.

scholarly journals Sentiment-based predictions of housing market turning points with Google trends

2016 ◽  
Vol 9 (1) ◽  
pp. 108-136 ◽  
Author(s):  
Marian Alexander Dietzel
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Turning Points ◽  
House Price ◽  
Data Set ◽  
Content Type ◽  
Forecasting Models ◽  
Search Volume ◽  
Out Of Sample ◽  
Google Search

Purpose – Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts. Design/methodology/approach – Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability. Findings – The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes. Practical implications – The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value – This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

scholarly journals International Real Estate Review

2018 ◽  
Vol 21 (2) ◽  
pp. 251-274
Author(s):  
Yuming Li ◽  
◽  
Jing Yang ◽  
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
House Price ◽  
Excess Risk ◽  
Single Family ◽  
Price Changes ◽  
The Real Estate ◽  
Long Run ◽  
Short Run ◽  
The U.S

We examine the relation between risk and returns in the U.S. residential housing market. We find that the risk of house price changes and the magnitude relative to the risk of income changes vary with economic conditions. We measure the excess risk of house price changes by adjusting for the risk of income changes and economic variables associated with the real estate and financial sectors of the economy, and find a significant and positive relation between house price changes and excess risk. We also find that excess risk has significantly adverse effects on the short-run momentum and long-run reversal of house price changes across metro areas, thus implying that excess risk induces price rigidity and helps to explain for the serial correlations in price changes in the U.S. single-family housing market.

scholarly journals House Price Beliefs And Mortgage Leverage Choice

2018 ◽  
Vol 86 (6) ◽  
pp. 2403-2452 ◽  
Author(s):  
Michael Bailey ◽  
Eduardo Dávila ◽  
Theresa Kuchler ◽  
Johannes Stroebel
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Heterogeneous Beliefs ◽  
The Other ◽  
Cross Sectional ◽  
Financial Decisions ◽  
The Relationship ◽  
The U.S

Abstract We study the relationship between homebuyers’ beliefs about future house price changes and their mortgage leverage choices. Whether more pessimistic homebuyers choose higher or lower leverage depends on their willingness and ability to reduce the size of their housing market investments. When households primarily maximize the levered return of their property investments, more pessimistic homebuyers reduce their leverage to purchase smaller houses. On the other hand, when considerations such as family size pin down the desired property size, pessimistic homebuyers reduce their financial exposure to the housing market by making smaller downpayments to buy similarly-sized homes. To determine which scenario better describes the data, we investigate the cross-sectional relationship between house price beliefs and mortgage leverage choices in the U.S. housing market. We use plausibly exogenous variation in house price beliefs to show that more pessimistic homebuyers make smaller downpayments and choose higher leverage, in particular in states where default costs are relatively low, as well as during periods when house prices are expected to fall on average. Our results highlight the important role of heterogeneous beliefs in explaining households’ financial decisions.

scholarly journals International Real Estate Review

2014 ◽  
Vol 17 (1) ◽  
pp. 109-135
Author(s):  
Marsha J. Courchane ◽  
◽  
Cynthia Holmes ◽  
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
House Prices ◽  
House Price ◽  
Price Indices ◽  
Real Estate Markets ◽  
House Price Indices ◽  
Mortgage Interest Rates ◽  
Precipitous Decline ◽  
The U.S

Canadian and U.S. real estate markets have compared similarly along dimensions such as inflation, mortgage interest rates, population and income growth and other measures. With respect to house prices, however, the series have moved in similar ways at some times, but then significantly diverged by the second quarter of 2007. For example, Canadian and U.S. house price indices reached essentially identical levels in 1987Q2, 1995Q1 and 2007Q2. As a consequence of the U.S. financial crisis and precipitous decline in house prices, the U.S. and Canadian indices have sharply diverged. Our paper examines whether or not the house price indices were driven by fundamentals during these time periods, or whether they diverged from fundamentals. We find that the U.S. house prices closely aligned with fundamentals until the mortgage markets crashed in 2008. We find that Canadian house prices continue to align with fundamentals. However, there have been some significant market changes between the two countries and key housing market measures indicate that Canadian markets are now moving along some paths similar to those taken by the U.S. prior to the crash.

scholarly journals MONETARY LIQUIDITY AND THE BUBBLES IN THE U.S. HOUSING MARKET

2015 ◽  
Vol 19 (1) ◽  
pp. 1-12 ◽  
Author(s):  
I-Chun TSAI
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Asset Bubbles ◽  
Price Bubbles ◽  
Current Period ◽  
Housing Bubbles ◽  
The Mean ◽  
The U.S

Extant studies indicate that the excessive easing of monetary supplies can result in surplus liquidity, which can consequently facilitate the formation of asset bubbles. This study references data on house prices in the U.S. from January 1991 to August 2012 to explore the correlations between monetary liquidity and house price bubbles in the U.S. housing market. Fluctuations in house prices are classified as related to either fundamentals (the mean reversion behavior and responses to information of the current period) or bubbles (self-related behavior). Results show a significant correlation between the formation of housing bubbles and monetary supplies. Long-term easing of monetary supplies can cause housing marketing returns to deviate from fundamentals, which then results in an increase in continuous fluctuations in house prices and the likelihood of the formation of house price bubbles.

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.

scholarly journals POWER LAWS IN REAL ESTATE PRICES DURING BUBBLE PERIODS

2012 ◽  
Vol 16 ◽  
pp. 61-81 ◽  
Author(s):  
TAKAAKI OHNISHI ◽  
TAKAYUKI MIZUNO ◽  
CHIHIRO SHIMIZU ◽  
TSUTOMU WATANABE
Keyword(s):  
Real Estate ◽  
Power Law ◽  
House Price ◽  
Power Laws ◽  
Cross Sectional ◽  
Price Distribution ◽  
Before And After ◽  
Real Estate Prices ◽  
Using Data ◽  
The U.S

How can we detect real estate bubbles? In this paper, we propose making use of information on the cross-sectional dispersion of real estate prices. During bubble periods, prices tend to go up considerably for some properties, but less so for others, so that price inequality across properties increases. In other words, a key characteristic of real estate bubbles is not the rapid price hike itself but a rise in price dispersion. Given this, the purpose of this paper is to examine whether developments in the dispersion in real estate prices can be used to detect bubbles in property markets as they arise, using data from Japan and the U.S. First, we show that the land price distribution in Tokyo had a power-law tail during the bubble period in the late 1980s, while it was very close to a lognormal before and after the bubble period. Second, in the U.S. data we find that the tail of the house price distribution tends to be heavier in those states which experienced a housing bubble. We also provide evidence suggesting that the power-law tail observed during bubble periods arises due to the lack of price arbitrage across regions.

scholarly journals Infodemiology of systemic lupus erythematous using Google Trends

Lupus ◽  
2017 ◽  
Vol 26 (8) ◽  
pp. 886-889 ◽  
Author(s):  
M Radin ◽  
S Sciascia
Keyword(s):  
Big Data ◽  
Northern Hemisphere ◽  
Lupus Erythematosus ◽  
Kendall Test ◽  
Google Trends ◽  
Data Monitoring ◽  
Search Term ◽  
Systemic Lupus ◽  
Search Terms ◽  
Google Search

Objective People affected by chronic rheumatic conditions, such as systemic lupus erythematosus (SLE), frequently rely on the Internet and search engines to look for terms related to their disease and its possible causes, symptoms and treatments. ‘Infodemiology’ and ‘infoveillance’ are two recent terms created to describe a new developing approach for public health, based on Big Data monitoring and data mining. In this study, we aim to investigate trends of Internet research linked to SLE and symptoms associated with the disease, applying a Big Data monitoring approach. Methods We analysed the large amount of data generated by Google Trends, considering ‘lupus’, ‘relapse’ and ‘fatigue’ in a 10-year web-based research. Google Trends automatically normalized data for the overall number of searches, and presented them as relative search volumes, in order to compare variations of different search terms across regions and periods. The Menn–Kendall test was used to evaluate the overall seasonal trend of each search term and possible correlation between search terms. Results We observed a seasonality for Google search volumes for lupus-related terms. In the Northern hemisphere, relative search volumes for ‘lupus’ were correlated with ‘relapse’ (τ = 0.85; p = 0.019) and with fatigue (τ = 0.82; p = 0.003), whereas in the Southern hemisphere we observed a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.85; p = 0.018). Similarly, a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.70; p < 0.001) was seen also in the Northern hemisphere. Conclusion Despite the intrinsic limitations of this approach, Internet-acquired data might represent a real-time surveillance tool and an alert for healthcare systems in order to plan the most appropriate resources in specific moments with higher disease burden.

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.

International Real Estate Review

2009 ◽  
Vol 12 (3) ◽  
pp. 193-220
Author(s):  
Karol Jan Borowiecki ◽  
Keyword(s):  
Real Estate ◽  
Positive Impact ◽  
House Prices ◽  
House Price ◽  
Housing Price ◽  
Quality Improvements ◽  
Vector Autoregressive ◽  
Price Growth ◽  
Price Determinants ◽  
A Minor

This paper studies the Swiss housing price determinants. The Swiss housing economy is reproduced by employing a macro- series from the last seventeen years and constructing a vector-autoregressive model. Conditional on a comparatively broad set of fundamental determinants considered, i.e. wealth, banking, demographic and real estate specific variables, the following findings are made: 1) real house price growth and construction activity dynamics are most sensitive to changes in population and construction prices, whereas real GDP, in contrary to common empirical findings in other countries, turns out to have only a minor impact in the short-term, 2) exogenous house price shocks have no long-term impacts on housing supply and vice versa, and 3) despite the recent substantial price increases, worries of overvaluation are unfounded. Furthermore, based on a self-constructed quality index, evidence is provided for a positive impact of quality improvements in supplied dwellings on house prices.

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