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Time-varying Persistence of House Price Growth

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How does time-varying persistence in U.S. house price growth vary across regions?

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Image courtesy of interviewee. December 2, 2024

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This video discusses the dynamics of the housing market as an asset class, highlighting its distinct behavior compared to financial assets such as stocks. Unlike stocks, which follow a random walk process making their prices unpredictable, housing prices exhibit persistence—meaning if prices rise in one period, they are likely to continue rising in the next period. This video concept of price persistence was emphasized in a pioneering study by Case and Shiller in 1989.

The main focus of the presented research is to question whether this high persistence in housing prices is stable over time. The authors investigate whether the factors influencing this persistence change over time, drawing upon U.S. housing data since 1989. Their findings indicate that the level of price persistence is not stable; it varies significantly over time due to factors such as demand and supply dynamics in the housing market.

They specifically note a change around the mid-1990s coinciding with the deregulation of banking practices, which notably influenced credit supply and housing market behavior. The study highlights that regions with previously low persistence, particularly in the South and Midwest, experienced a significant increase in persistence post-deregulation. Future research is suggested to explore the variations in housing market cycles and to analyze more granular data to validate their findings further.

Overall, the study underscores the complexity of housing price dynamics and the impact of regulatory changes on the stability of price persistence over time.

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Here are five key points from the video:

1. **Differential Behavior of Housing Assets**: The housing market behaves quite differently from financial assets like stocks. While stock prices are often modeled as following a random walk, housing prices exhibit strong persistence, meaning if prices increase in one period, they are likely to increase in the next.

2. **Instability of Price Persistence**: The study challenges the notion that the high persistence and momentum of housing prices are stable over time. It finds that this persistence is influenced by various factors and is not consistent across different time periods.

3. **Impact of Deregulation**: The mid-1990s deregulation, particularly the Interstate Banking and Branching Efficiency Act, played a significant role in altering the persistence of housing prices, particularly in the South and Midwest regions. This deregulation led to changes in credit supply and demand dynamics in the housing market.

4. **Regional Variations**: The research emphasizes that changes in price persistence were particularly notable in certain regions (South and Midwest) that previously exhibited near-zero persistence. After the mid-1990s, these regions saw a significant increase in price persistence, which deviated from the behaviors observed in other parts of the country.

5. **Future Research Directions**: The study advocates for further research to explore whether the observed variations in housing market persistence will continue in future cycles and suggests the use of more granular data (such as city-level data) to better understand the underlying dynamics affecting housing prices.

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Here are some key quotes extracted from the video about the housing market and its behavior as an asset:

1. **Importance of the Housing Market**: "The housing market is a very kind of, important, asset market for everybody, around the world."

2. **Behavior of Housing as an Asset**: "Housing as an asset is known to behave quite differently from other assets, financial assets such as stocks."

3. **Persistence in Housing Price Movements**: "Case and Shiller's pioneering work back in 1989...show that the housing price movement has a strong persistence."

4. **Contrast with Rational Expectations Theory**: "This goes against the so-called Rational Expectation Theory...how the price movement is predictable to some degree, because it shows a so-called persistence."

5. **Findings on Stability Over Time**: "What we found is, is not, you know, stable over time...showing that instead, it changes over time depending on certain factors."

6. **Use of Data**: "FHFA price index...has several distinctive advantages...over commercially produced datasets."

7. **Factors Affecting Housing Prices**: "The price itself is determined by the interaction of the supply and demand...the factors in demand side changes quite often while the factors in supply side changes quite sluggishly."

8. **Impact of Deregulation**: "The persistence level in those markets...had shot up...dramatically from near zero to 0.5, 0.6."

9. **Future Research Directions**: "We are interested in exploring further whether this variation and also the underlying factor might sustain in different housing market cycles."

10. **Methodological Justification**: "It is well established in the literature that the housing market cycle behaves very similar to that of the business cycle model."

These quotes encapsulate the main themes and findings related to the persistence, variability, and market dynamics of housing prices as conveyed in the video.

X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:90000

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The housing market is a very kind of, important,

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asset market for everybody, around the world.

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And then the, housing as an asset is known

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to behave quite differently from other assets,

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financial assets such as stocks.

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So in the stock market, there is a well established,

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you know, you know, theorem that the, it, it,

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the stock prices are following.

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So-called random oak process.

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It means that the, in advance, it, it is impossible

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for us to predict how the stock price will move,

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but the, what you found in the housing market,

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especially, case

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and Shiller's pioneering work back in 1989,

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which was published in, a ER, they show

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that the housing price movement has a strong persistence,

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which goes against the so-called,

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rational Expectation Theory, according

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to the rational expectations.

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Uh, you know, since we use, make use

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of all the information in an advance, it becomes impossible

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to predict what will happen next.

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But, in contrast, how the price movement is,

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predictable to some degree,

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because it shows a so-called persistence.

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It means that,

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if ho house price grows faster this period,

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it is also likely

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to grow faster in the the following period, and vice versa.

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That's pretty much well established in the housing,

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literature.

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Our interest was actually, quite, you know,

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different from the, established one

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because we raised a question whether that high persistence

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or momentum

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or inertia of house price movement is a stable

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over time or not.

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Uh, indeed, what we found is, is not, you know,

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stable over time, and we provide some more,

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scientific evidence, in, in this paper using the,

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US data the year since 1989, the semi paper,

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which I just mentioned by case in sheer.

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So many people actually,

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provided, you know, evidence of, you know,

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high persistence in, especially in the US housing market,

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regardless of which data set they use,

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no matter which time period they study,

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and no matter which level of, data,

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desegregation they use, they commonly,

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found a high persistence, high persistence

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of house price movements.

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Our study is definitely, you know,

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distinguishing itself from the previous one by showing that

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that persistence is not stable.

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Instead, it,

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changes over time depending on certain factors,

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which we show in our paper.

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Yeah, FHFA, price index, house Price Index is used

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for our analysis because it has, several,

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distinctive advantages, over

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Commercially, produced, data sets such

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as a core logic or Zillow data,

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which are quite expensive

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and not publicly available, to researchers like me.

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And then, given that is index,

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it has base here at which the pro, the, the value is,

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fixed at a hundred,

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but what we used is the, change in the,

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in the price index, which is the, what

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so called house price growth rate, which we used.

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We used both the US national, series as well as,

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200, 50, eight, you know,

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urban house prices, which are, quite comprehensive,

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you know, including almost 80%

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of the total population of the United States.

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The mechanism is like this.

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Uh, so the,

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housing price itself has never been stable over time.

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I mean, the price itself is determined by the interaction

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of the supply and demand

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as you agree, in the literature.

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What, what, you know, researchers agree is that the,

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the factors in demand side,

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changes quite often while, the factors in supply side,

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changes quite sluggishly.

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So, they, they, you know, previous researchers,

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you know, point out that the,

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it might be demand fact,

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the demand side factors which might be causing that the,

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you know, persistence.

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And then, what we found is,

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what do you, hypothesize?

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Is that, given that the dose factors change over time, then,

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is it, isn't it conceivable that we assume that the,

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some underlying, mechanism

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of a persistence per se might be also, you know,

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changing over time or VEing over time?

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But, research is that the housing market,

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persistence, persistence is not stable over time.

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Uh, instead, it is a changing, over time, especially

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around the mid 1990s when the, there was a,

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a quite notable de deregulation in

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regarding the housing market, which is the, so-called IBVA,

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which stands for the, interstate banking

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and, branching Efficiency Act, which repeals the,

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the vis prohibition of the interstate,

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branching activities by banks.

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So that was pretty much, very well aligned with the,

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time variation we noticed in the, in the persistence.

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And in addition,

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when you look at the original price data,

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it is neither in the,

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in the northeast market, nor in the West market,

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but in the, south

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and the Midwest market shows a strong, change in the,

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persistence, which is a timely, well aligned with that,

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the deregulation.

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So the, we believe that the,

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debt deregulation has triggered, some,

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changes in the housing market, especially in terms of the,

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su

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and, credit supply, you know,

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expansion in the housing market, which we found in our,

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study that that is indeed the, mostly,

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probable factor behind this time variation.

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And then, so why did the, those, regional PRI

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and, housing markets in the South

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and, you know, Midwest, you know, behave differently?

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Uh, we found out that the,

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in those markets there were previously not,

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there's not much persistence.

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So at the, the persistence level in those markets

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were very close to zero.

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You know, zero means that there is no persistence,

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and also it behaves very similar to the,

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white noise, random of process.

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So, but since the, mid 1990s,

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their persistence level had, you know, shot up,

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you know, dramatically from, near zero to,

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0.5, 0.6.

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This is quite notable change we capture.

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And, we believe that the, the overall,

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persistence change over time,

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we observed from US national data must have been,

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mainly driven, driven by those two areas, not,

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not the rest of the, country.

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That's what we emphasize in our paper.

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And, for the future work, we believe

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that the housing, you know, market cycle

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is also changing over time as well.

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So since we used, up to the data point, you know, up

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to, 2020, we are interested in, you know,

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exploring further whether this, variation

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and also the, underlying factor might sustain in

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different, housing market cycles.

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And also if, if it's available, whether like

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to use some more disaggregated, data level than the,

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urban H House price.

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Uh, you know, data,

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because the, at, at, at the city level MSA level,

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it is already, you know,

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there's a huge within city, had chosen adding.

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So we'd like to see some more, granular data analysis,

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to see whether our, conclusion

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and our findings sustains.

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The reason why we think this, this methodology is,

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are suitable for our analysis are twofold.

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First, it is well established in the literature that the,

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housing market cycle behaves very similar to that

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of the business cycle, you know, model.

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And then because the, macro economist have shown

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that, this methodology is well, suited

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for character characterizing that the,

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business cycle, we believe that this is also working

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for the housing market as well.

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And second, as we know that the, housing

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and the rent account for the, line share in the CPI ON

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and other, you know, the cost of living measures.

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So the, given that the macro economists

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Have shown that the, this,

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time bearing premier model is, well capturing the,

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behavior of, inflation.

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So we, we think that this is quite, well aligned with,

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our focus on the, house price movements.

Understanding Housing Market Dynamics: Persistence vs. Randomness

00:00:00 - The housing market is a very kind of, important, asset market for everybody, around the world. And then the, housing as an asset is known to behave quite differently from other assets, financial assets such as stocks. So in the stock market, there is a well established, you know, you know, theorem that the, it, it, the stock prices are following. So-called random oak process. It means that the, in advance, it, it is impossible

00:00:36 - but the, what you found in the housing market, especially, case and Shiller's pioneering work back in 1989, which was published in, a they show that the housing price movement has a strong persistence, which goes against the so-called, rational Expectation Theory, according to the rational expectations. you know, since we use, make use of all the information in an advance, it becomes impossible

00:01:08 - But, in contrast, how the price movement is, predictable to some degree, because it shows a so-called persistence. It means that, if ho house price grows faster this period, it is also likely to grow faster in the the following period, and vice versa. That's pretty much well established in the housing, literature. Our interest was actually, quite, you know,

Instability of Housing Price Persistence Over Time

00:01:39 - because we raised a question whether that high persistence or momentum or inertia of house price movement is a stable over time or not. indeed, what we found is, is not, you know, stable over time, and we provide some more, scientific evidence, in, in this paper using the, US data the year since 1989, the semi paper, which I just mentioned by case in sheer.

00:02:11 - provided, you know, evidence of, you know, high persistence in, especially in the US housing market, regardless of which data set they use, no matter which time period they study, and no matter which level of, data, desegregation they use, they commonly, found a high persistence, high persistence of house price movements. Our study is definitely, you know, distinguishing itself from the previous one by showing that

00:02:42 - Instead, it, changes over time depending on certain factors, which we show in our paper. Yeah, FHFA, price index, house Price Index is used for our analysis because it has, several, distinctive advantages, over Commercially, produced, data sets such as a core logic or Zillow data, which are quite expensive and not publicly available, to researchers like me.

Analyzing Housing Price Dynamics and Persistence

00:03:12 - it has base here at which the pro, the, the value is, fixed at a hundred, but what we used is the, change in the, in the price index, which is the, what so called house price growth rate, which we used. We used both the US national, series as well as, 200, 50, eight, you know, urban house prices, which are, quite comprehensive,

00:03:42 - of the total population of the United States. The mechanism is like this. so the, housing price itself has never been stable over time. I mean, the price itself is determined by the interaction of the supply and demand as you agree, in the literature. What, what, you know, researchers agree is that the, the factors in demand side, changes quite often while, the factors in supply side, changes quite sluggishly. So, they, they, you know, previous researchers,

00:04:16 - it might be demand fact, the demand side factors which might be causing that the, you know, persistence. And then, what we found is, what do you, hypothesize? Is that, given that the dose factors change over time, then, is it, isn't it conceivable that we assume that the, some underlying, mechanism of a persistence per se might be also, you know, changing over time or VEing over time? But, research is that the housing market,

Time-Variation in Housing Market Persistence Post-Deregulation

00:04:50 - instead, it is a changing, over time, especially around the mid 1990s when the, there was a, a quite notable de deregulation in regarding the housing market, which is the, so-called IBVA, which stands for the, interstate banking and, branching Efficiency Act, which repeals the, the vis prohibition of the interstate, branching activities by banks.

00:05:20 - time variation we noticed in the, in the persistence. And in addition, when you look at the original price data, it is neither in the, in the northeast market, nor in the West market, but in the, south and the Midwest market shows a strong, change in the, persistence, which is a timely, well aligned with that, the deregulation.

00:05:49 - debt deregulation has triggered, some, changes in the housing market, especially in terms of the, su and, credit supply, you know, expansion in the housing market, which we found in our, study that that is indeed the, mostly, probable factor behind this time variation. And then, so why did the, those, regional PRI and, housing markets in the South and, you know, Midwest, you know, behave differently?

Evolution of Housing Market Persistence

00:06:22 - in those markets there were previously not, there's not much persistence. So at the, the persistence level in those markets were very close to zero. You know, zero means that there is no persistence, and also it behaves very similar to the, white noise, random of process. So, but since the, mid 1990s, their persistence level had, you know, shot up, you know, dramatically from, near zero to, 0.5, 0.6.

00:06:54 - And, we believe that the, the overall, persistence change over time, we observed from US national data must have been, mainly driven, driven by those two areas, not, not the rest of the, country. That's what we emphasize in our paper. And, for the future work, we believe that the housing, you know, market cycle

00:07:24 - So since we used, up to the data point, you know, up to, 2020, we are interested in, you know, exploring further whether this, variation and also the, underlying factor might sustain in different, housing market cycles. And also if, if it's available, whether like to use some more disaggregated, data level than the, urban H House price. you know, data, because the, at, at, at the city level MSA level,

Analyzing Housing Market Methodologies

00:07:57 - there's a huge within city, had chosen adding. So we'd like to see some more, granular data analysis, to see whether our, conclusion and our findings sustains. The reason why we think this, this methodology is, are suitable for our analysis are twofold. First, it is well established in the literature that the, housing market cycle behaves very similar to that of the business cycle, you know, model.

00:08:29 - that, this methodology is well, suited for character characterizing that the, business cycle, we believe that this is also working for the housing market as well. And second, as we know that the, housing and the rent account for the, line share in the CPI ON and other, you know, the cost of living measures. So the, given that the macro economists Have shown that the, this,

00:09:07 - behavior of, inflation. So we, we think that this is quite, well aligned with, our focus on the, house price movements.

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