Bubble, Bubble, Where's the Housing Bubble?

Book Review

Shenzhen is one of cities that is real estate market-oriented, and the real estate is an important industry in the economic development. Real estate price statistics have drawn more and more attention and is being questioned due to distortion of announced real estate price in recent years. For example, the new commercial house price statistics is measured by using a simple average of price statistics, but the sample's heterogeneity leads to the house prices by statistics inconsistent with the real house prices. Meanwhile, in the process of second-hand housing transactions, the transaction prices is distortion and real house prices are out of control, caused by fake contracts. In order to grasp changes and trends of the price in the real estate market accurately, this research designs a house price indexes model, which covers new and secondary house in Shenzhen based on the real estate database. The Shenzhen house price indexes system employs B/S and three-layer architecture. It is based on the Skyline platform, and utilizes the JavaScript language for secondary development. The Shenzhen house price indexes system can effectively integrate real estate data, spatial data, 3D simulation model and house price indexes model, which achieves to monitor, manage, and analyze real estate data, and house price indexes. The system prepares and publishes periodic house price indexes, which could provide the latest, timeliest, and most accurate real estate price information for the parties in the real estate market.

For many analysts, the Chinese economy is being spurred on by a bubble in the housing market, probably driven by the fiscal stimulus package and massive credit expansion, with potentially adverse effects on the real economy. The house price development is investigated by panel cointegration techniques. Evidence is based on a data-set for 35 major cities. Cointegration is detected between real house prices and a set of macroeconomic determinants. The results indicate that the bubble is less than 15 per cent of the equilibrium value implied by the fundamentals at the end of 2010. The bubble is particularly huge in the cities in the south-east coastal areas and special economic zones. While the spillovers from real house prices to consumer price index (CPI) inflation are significant at the margin, gross domestic product (GDP) growth may not be heavily affected. Thus, a decline of the bubble will likely have only modest effects on the real economy.

Factors related to non-primary housing ownership: Evidence from China General Social Survey

Heterogeneous traders, house prices and healthy urban housing market: A DSGE model based on behavioral economics

PurposeThis study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.Design/methodology/approachThis study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla’s and Amazon’s new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.FindingsThe results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla’s relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon’s relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation.Originality/valuePrevious literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.

ABSTRACTA new empirical approach to identify local housing markets (LHM’s) is proposed, which focuses on the spatial correlation between local house price indices constructed from repeat sales data. It extends the work of Pryce who claimed that if housing in different locations are perfect substitutes, their house price indices should be perfectly correlated over time. Pryce’s work represents a paradigmatic change in identifying local housing markets using revealed preferences rather than hedonic pricing. It requires spatial panel data for house prices which we construct using repeated sales data to generate house price indices for Tel Aviv (1998–2014) for over 100 census tracts. These price indices are used to define LHMs. The number of LHMs varies inversely with the degree to which house prices in locations belonging to the same LHM, are expected to be correlated. It also varies directly with the order of contiguity of these locations. Results point to considerable spatial heterogeneity in house price movement. This belies the popular impression that the Tel Aviv housing market is relatively homogeneous, characterised by expensive housing and uniform house price movements.

Purpose Housing provision is a fundamental driver of national development and economic prosperity. However, housing affordability remains a critical problem worldwide, affecting countries regardless of their level of development, whether developed, developing or underdeveloped. The housing price index (HPI) measures trends in residential property prices and provides valuable insights into market dynamics. This study aims to model and forecast the HPI in Malaysia. Accurate HPI predictions are essential for informing policy decisions, guiding investment strategies and ensuring sustainable housing market development. Design/methodology/approach The autoregressive integrated moving average (ARIMA) model was used to analyse historical housing price data for modelling and forecasting housing prices for different types. The accuracy and reliability of the models and forecasts were assessed using key statistical metrics, including variance (s²), log-likelihood, Akaike information criterion, corrected Akaike information criterion and Bayesian information criterion. Findings The findings demonstrate that the ARIMA model improves accuracy in capturing seasonal and trend components in the Malaysian housing market, offering actionable insights for strategic decision-making. In addition, this research presents point and annual changes across different housing types that seek to offer a detailed view of price movements. By providing a reliable five-year forecast of the HPI, this study enables more informed decision-making in housing policy, investment strategy and financial planning. Practical implications Policymakers can use these insights to stabilise housing markets, whereas investors and developers can anticipate market movements for better capital allocation. Homeowners and prospective buyers will benefit from understanding future price trends, allowing for more strategic decisions regarding property purchases or sales. Originality/value The originality of the study lies in its ability to empirically forecast housing prices for various types of housing for the next five years. This provides valuable guidance for housing-related decisions. This research contributes to the growing literature on real estate forecasting in emerging markets and underscores the relevance of advanced time series models in navigating the complexities of dynamic housing markets.

The volatility of the housing price based on demand and supply in the housing market reflects the structural features of the housing market. Consequently, decomposing the housing market into trends and cycles through econometric models will make it possible to deduce the nature of the housing market in the long term, in addition to provide hindsight for real estate portfolio asset allocation in a more systematic way.BRBR The paper addresses the housing price behavior through univariate time series analysis. The Unobserved-Component (UC) model and the Beveridge-Nelson (BN) decomposition model are applied to analyze the factors for the volatility of housing price with the trends and cycles. Specifically, the UC model is analyzed by UC-Zero(0) model assuming the covariance between the trends and the cycles, which are unobserved variables, is zero (0), and the UC-UR (UC-UnRestricted) model, which is free from the restraint of the covariance between the trends and cycles. The estimation is based on the Kalman Filter. The study analyzes how the volatility in the domestic housing price index was influenced by the trends and cycles, which are inherent variables, by comparing the estimated values of the UC-0 and UC-UR model with the BN decomposition model. Domestic housing prices have plunged two times: the 1997 Asian financial crisis and the 2008 Global financial crisis. However, the analysis data in this research include only the 2008 Global financial crisis.BRBR The results are summarized as follows: First, the volatility of the domestic housing price index can be explained by the UC-0 model. As explained by the UC-0 model with the assumption of the covariance between trend and cycles zero(0), the domestic housing price index has not shown any correlation between the trend and cycle, and thus it implies that the trends mainly explain the volatility in the housing price index. Second, as the trends mainly explain the volatility in housing prices, domestic housing prices do not follow a random walk under the efficient market hypothesis. Hence, the price volatility can be seen as predictable, and the right-upward price trend that has been lasted for a considerable period of time can be seen as a result of reflecting adaptive expectations of market participants. Lastly, institutional investors such as pension funds are increasing their share of real estate and alternative investments, and the share of real estate is increasing in the asset market. The fact that inefficient housing market, and the housing price return as seen through the housing price analysis follow a very differential distribution from the normal distribution would mean that as a factor hindering investors

This paper examines the role of nonfundamentals‐based sentiment in house price dynamics, including the well‐documented volatility and persistence of house prices during booms and busts. To measure and isolate sentiment's effect, we employ survey‐based indicators that proxy for the sentiment of three major agents in housing markets: home buyers (demand side), home builders (supply side), and lenders (credit suppliers). After orthogonalizing each sentiment measure against a broad set of fundamental variables, we find strong and consistent evidence that the changing sentiment of all three sets of market participants predicts house price appreciation in subsequent quarters, above and beyond the impact of changes in lagged price changes, fundamentals, and market liquidity. More specifically, a one‐standard‐deviation shock to market sentiment is associated with a 32–57 basis point increase in real house price appreciation over the next two quarters. These price effects are large relative to the average real price appreciation of 71 basis points per quarter observed over the full sample period. Moreover, housing market sentiment and its effect on real house prices is highly persistent. The results also reveal that the dynamic relation between sentiment and house prices can create feedback effects that contribute to the persistence typically observed in house price movements during boom and bust cycles.

In a previous paper (Taltavull and White, 2012) we analysed the role of money supply, migration and mortgage finance in house price evolution. Using a VECM framework we examined these variables together with income, inflation, and interest rates for both Spain and the UK. This followed on from research by other authors including Muellbauer (2007), Meen and Andrew (2003), and Mishkin (1995). The results indicated an important role for income, mortgages and migration in UK house price movements, more so than in Spain. In this paper we build upon our previous work and focus on the channels through which liquidity shocks can affect house prices. Lastrapes (2002) argues that money supply affects real house prices. Greiber and Setzer (2007) find that liquidity is important for new supply and hence house prices identifying money demand, asset price and credit channels. In the money demand channel, three different effects are present, wealth, substitution and transactions effects. This permits us to test of changes in house prices lead to changes in money supply. In the asset price channel we test if changes in money supply cause house price change, and in the credit channel, higher housing collateral can increase loan amounts. In this paper we test different channels to identify which is more or less important and whether this varies across countries thus identifying the impact that the housing market has on the macroeconomy and from which source this is derived.

Bubbles in Real Estate? A Longer-Term Comparative Analysis of Housing Prices in Europe and the Us

Tracking the Housing Bubble Across Metropolitan Areas – A Spatio-Temporal Comparison of House Price Indices

Monetary Policy, Capital Inflows and the Housing Boom

Bubbles in the Australian housing market