In the following 5 chapters, you will quickly find the 34 most important statistics relating to "U. Housing Market". Skip to main content Try our corporate solution for free! Single Accounts Corporate Solutions Universities. Real Estate. Published by Statista Research Department , Oct 4, The U. National Home Price Index grew rapidly from to , when they reached their peak, and then started to fall down in the following years.
The house price growth trend began in again and since the house price index has exceeded its value from In January , the average new home was sold for , U. There were , houses sold in the United States in - the largest figure since Home flipping is a real estate term which refers to the practice of an investor buying property with the aim of reselling them for a profit. In the second quarter of , the number of single family homes and condos flipped reached 80, in the United States.
The leading house flipping markets by gross return on investment were Oklahoma City, Fargo, and Pittsbourgh. This text provides general information. Statista assumes no liability for the information given being complete or correct. Due to varying update cycles, statistics can display more up-to-date data than referenced in the text. Number of housing units in the U. Number of owner-occupied housing units in the U.
Existing home sales fell slightly by 1. Likewise, new homes sold fell by This lack of supply is unlikely to be resolved over the next 5 to 10 years without more aggressive incentives for builders to add new units.
The total number of existing homes available for sale fell by Existing homes inventory was only at 2. A reading of 50 is the midpoint between positive and negative. Sentiment stood at 83 in September and reached a record high of 90 in November. It then dropped dramatically in the following months, as lumber prices surged and supply chain disruptions hampered construction activity.
The U. Last year, the U. The unemployment rate fell to 5. Bureau of Labor Statistics. But it remains higher than the average unemployment rate of 4. The last housing crash began in Q2 There was a Phoenix registered the biggest drop And despite the pandemic, the U. Phoenix led the boom, with house prices surging by Existing home sales which include single-family homes, townhomes, condominiums and coops stood at a seasonally adjusted annual rate of 5.
Residential properties typically stayed on the market for 17 days in August , down from 22 days a year earlier, according to NAR. With improved homebuilder sentiment, residential construction activity is now rising strongly again. During the previous boom, completions peaked at almost 2,, units in , but crashed to , units in Existing homes inventory was at 2. The equivalent inventory of new houses for sale was 6. The foreclosure rate was 0. Delaware had the highest foreclosure rate in the country in H1 , at 0.
Likewise, the delinquency rate on single-family mortgages fell to 2. In its recent meeting, the Fed indicated that it intends to hold the key rate to near zero despite rising inflationary pressures. In the past three months, inflation averaged 5.
Federal Reserve Chair Jerome Powell warned that as the economy reopens, an upward pressure on prices is expected due to supply bottlenecks in some sectors.
Mortgage debt outstanding rose by 5. Federal Reserve System. The size of the mortgage market was equivalent to about Rising rents are another sign of healthy economic fundamentals.
The median asking rent in the U. Census Bureau - the highest level ever recorded. Median rents were more or less static from to , according to the U. However since , rents have risen at least as fast as house prices. Despite the pandemic, the nationwide rental vacancy rate dropped to 6. But in Q2 , the rental vacancy rate rose to 6. The West had the lowest rental vacancy rate of 4. It was followed by the Northeast 5. The Midwest had the highest rental vacancy rate of 7.
The homeownership rate in the U. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market — at the state level based on the Random Matrix Theory RMT.
We identify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices.
We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles. Because houses and apartments are tradeable and are commonly used in speculations, they are considered as a special kind of commodity.
As time passes the house prices boom and bust. Because the housing market is closely related to the financial system and plays a crucial role in economies, a crash of the housing market usually has disastrous consequences, causing financial crisis and economic recession. Recent examples include the — Asian crisis 1 , 2 , 3 and the — global financial tsunami followed by the — global recession and the European sovereign-debt crisis, none of which has ended 4. When the correlations among the constituents of a market become stronger and the ripple effect increases 5 , prices tend to converge 6 and the systemic risk increases.
However, there is evidence showing that alternative measures based on eigenvalues and eigenvectors of the correlation matrix outperform the average correlation in characterizing market integration 7 , quantifying systemic risks measured by means of the absorption ratio 8 and constructing profitable investment portfolios 8 , 9.
Hence, it is extremely important to understand the spatiotemporal dynamics of housing markets through an investigation of the correlation matrix of price growth rates. The correlation matrices of stock returns and indices have been widely studied in different markets The studies have employed variety of methods ranging from the minimal spanning trees 11 , the planar maximally filtered graph 12 based on distance matrices, to RMT 13 , All methods can be used to identify constituent clusters in financial systems When RMT is used to investigate the correlation structure of financial markets, the largest eigenvalue serves to explain the collective behavior of the market and other eigenvalues are commonly used to explain clustering of stocks or indices into groups with specific traits.
The correlation matrices of housing markets are rarely studied, mainly due to the short length of house price indices, where the sampling frequency is usually either monthly or quarterly. Using the RMT framework at the state level, we investigate the spatiotemporal dynamics of the US housing market. We analyze the All-Transactions Indices of the 50 states and the District of Columbia published by the Federal Housing Finance Agency, which estimate sales prices and appraisal data. The logarithmic return at time t is defined as.
Stock markets are characterized by both fast and slow dynamics 15 , To estimate the empirical correlation matrix and minimize the unavoidable statistical uncertainty, we use a large window containing a large number of data points. Figure 1 A shows the average correlation coefficient of Eq. In recent years the average correlation coefficient has sharply increased indicating that the US housing market has become strongly correlated.
In the early years of the period studied, we find that only a small number of states exhibit correlated housing indices. In contrast, we find a sharp increase in housing market correlations over the past decade, indicating that systemic market risk has also greatly increased. The error bar is the standard deviation of the PDF at each time t. For the evolution of the PDF, see Fig. The five vertical dashed lines corresponding to the five regime-shift points. An important topic in economic theory is whether housing market bubbles and financial bubbles in general are predictable.
In addition to using the average correlation coefficient we can also measure systemic risk using the absorption ratio , which is a better approach because perfectly integrated markets can exhibit weak correlation 7 , 8 , 9.
Figure 1 C shows the absorption ratio. Note that the increase in systemic risk is approximately linear, even during the burst of the housing bubble in , indicating that the US housing market continues to be fragile and unstable.
To investigate the possible collective market effect embedded in the deviating eigenvalues, we compare the returns of the eigenportfolio with the US HPI returns see Methods. Thus although the largest eigenvalue reflects the behavior of the stock market, the other eigenvalues do not. S3 , which differ substantially from the results obtained for stock markets. We find that the two regime-shift points in Fig. Therefore, in the regimes corresponding to the first and last time periods, the market effect quantified by the correlation coefficient k 1 is remarkable.
In contrast, the market effect is much weaker in the second time period Fig. Within the second time period, we further identify a regime-shift point between Q 1 and Q 2, where k 1 drops from 0. Each plot shows the evolution of the correlation coefficient k n t between R n and R in each moving window.
The blue symbols are estimated using ordinary least-squares linear regression, while the red ones are estimated using robust fitting. The four vertical lines indicate four regime-shift points between Q 3 and Q4, between Q 1 and Q2, between Q 2 and Q3 and between Q 2 and Q3, separating five different regimes.
The shading area in each plot means that the associated eigenvalue contains a market effect in the corresponding time period. See Fig. S3 for the scatter plots of R n against R. Using the four regime shifts in Figs.
The five regimes are separated by four regime shift points: between Q 3 and Q 4, between Q 1 and Q2, between Q 2 and Q 3 and between Q 2 and Q 3, where is visible in the evolution of k 1 , k 2 , k 3 and k 4 , is visible in the evolution of k 1 , k 2 and k 3 , is visible only in the evolution of k 4 and is visible in the evolution of k 1 , k 2 and k 4.
The cross-validation of the four regime shift points in the four plots of Fig. We find that the components of the eigenvector of the largest eigenvalue are positive in stock markets when the components exhibit small fluctuations over time, indicating a market effect.
The rest of the eigenvectors of other largest eigenvalues describe different clusters of stocks or industrial sectors 18 , 19 , For the US housing market, we find that the eigenvectors of the largest eigenvalues contain much richer information Fig. The existence of five regimes to is clear and the eigenvector components persist in each regime see Methods.
Moreover, the graphical approach in Fig. The five regimes to are visible. Moreover, we observe that the regime can be separated into two regimes at Q 1 to Q 2 according to the evolution of u 3. Starting with the first eigenvector u 1 , we study its components over time for different regimes. We find that in regime almost all the components of u 1 are positive. In contrast, Fig. During the period from Q 4 to Q 2, positive components of u 1 approximately correspond to the states in the Eastern half of the US and with California and Arizona in the Western US.
As time passes, transferring from regime to regime , states with initially negative components turn from negative to positive components. For the eigenvector u 2 in the first two regimes and we find a comparable number of negligible positive and negative components and it is not completely clear what information is contained in the US states with positive and negative components.
At approximately Q 2 the number of states with negative u 2 components drop significantly, leaving the majority of states with positive components that reflect a market effect. This predomination of positive components over negative components persists in and.
Beginning in late , the u 2 components of Washington and California switch from positive to negative and a few Northeastern states do the same. In regime , the two state clusters, one with positive and the other with negative u 2 components, approximately correspond to states with low and high HPI growth rates, respectively, as identified by the super-exponential growth model In the evolution of the eigenvector u 3 , we find two interesting features.
First, the majority of states in regime have positive components, reflecting a modest market effect. Second, there is an evident subregime around Q 2, which surprisingly corresponds to the onset of the primary US mortgage crisis.
The information contained in other regimes is ambiguous and it is difficult to extract clear information from the evolution of the fourth eigenvector u 4. To better understand the spatiotemporal dynamics of the US housing market at the state level, we partition the states into clusters for each time t.
Because there is a strong market effect in the correlation matrices, the Pearson correlation coefficient between the return time series r i and r j of two US states i and j may not reflect their intrinsic relationship, but may reflect the influence of the overall US HPI return r us on i and j 22 , We thus utilize a clustering algorithm that uses the corresponding partial correlation matrices P t by removing the market effect.
In this way we obtain a partial correlation matrix P t and an affinity A t for each t see Methods. For each t , we rearrange the order of states in P t and C t to be the same as in A t. The evolution of the three matrices is illustrated in Fig. In the early years represented by regions and , we identify the state clusters Fig.
These properties are consistent with the fact that the average cross-correlation level among US states is very low, indicating that the housing markets of different US states are to some extent isolated. With the development of the US housing market during the period Q 4— Q 1, more US states enter two different clusters of significantly different sizes Fig.
This period roughly corresponds to the two regimes and. During this period both clusters are relatively stable Fig. In regime the smaller cluster further splits into two even smaller clusters which remain relatively stable. At approximately Q 2 the larger cluster splits into two clusters of comparable size, but shortly after the two smaller clusters merge back into one Fig. Finally we find three stable clusters of similar size that form the sixth regime.
The order of the states is the same for the three matrices in each row. The ending quarters t of the windows from top to bottom are Q 4, Q 2, Q 4, Q 3 and Q 3.
Each cluster is represented by a colorful symbol. The determination of symbols and their coloring is explained in Methods. The states in a certain cluster are assigned with a cluster-specific colorful symbol and no symbol is assigned to those states not in any cluster. The colorful symbols have the same meaning as those in d. For each window t there are up to four clusters of states and the number of states in each cluster varies from one window to the next.
For each cluster, one of the four deviating eigenvalues makes a dominant contribution Fig. Figure 4 e shows the spatiotemporal dynamics of the state clusters. The states in the red cluster tend to have larger price fluctuations and a higher price value and the states in the green cluster exhibit smaller HPI growth rate fluctuations Fig.
In the earlier years and the clusters are unstable with a large number of states shifting between clusters. In contrast, there are more eigenvalues contributing to the red cluster. This phase-transition-like phenomenon in may have been evidence of a fast ripple effect within the US housing market. The time period of these two transitions corresponds to the downturn in the US housing market.
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