<p>It won't help much with the high frequency variation of interest to you but this is a famous and important paper on this topic:</p>

<blockquote>
  <p>I process satellite-generated data on terrain elevation and presence
  of water bodies to precisely estimate the amount of developable land
  in U.S. metropolitan areas. The data show that residential development
  is effectively curtailed by the presence of steep-sloped terrain. I
  also find that most areas in which housing supply is regarded as
  inelastic are severely land-constrained by their geography.
  Econometrically, supply elasticities can be well characterized as
  functions of both physical and regulatory constraints, which in turn
  are endogenous to prices and demographic growth. Geography is a key
  factor in the contemporaneous urban development of the United States.</p>
</blockquote>

<p>The Geographic Determinants of Housing Supply (Saiz (2010))</p>

<p>This paper may also be useful:</p>

<blockquote>
  <p>The long-run price elasticity for alternative specifications of new
  housing supply is estimated using U.S. annual data for 1950 through
  1994. The basic model expresses residential construction as a linear function of new housing price and the prices of construction inputs.
  Long-run elasticities range from 1.6 to 3.7, suggesting that new
  housing supply is price elastic. Residential construction responds to
  both the real interest and expected inflation rates, but other
  construction cost variables perform poorly. However, the results are
  sensitive to the time-series processes underlying the variables. A
  modified model that expresses residential construction as a function
  of changes in input prices, rather than their levels, produces a
  long-run elasticity of about 0.8 and a significant inverse
  relationship between new housing supply and the construction wage
  rate.</p>
</blockquote>

<p>The Long-Run Elasticity of New Housing Supply in the United States: Empirical Evidence for 1950 to 1994 (<a href="http://link.springer.com/article/10.1023/A:1007781228328" rel="nofollow">Blackley (1999)</a>)</p>

CC BY-SA 3.0
2015-07-08T18:00:30.350Z
2015-07-08T18:00:30.350Z
BKay
<p>Is there any existing literature on the various factors affecting the number of houses constructed in a particular city over, say, a given month? Factors might include: quality of housing stock, a lagging value of housing sale price, house price to earnings ratio. </p>

<p>Preferably, I would like to see literature that describes the relative importance of these factors, perhaps by running a regression--so that way, when I do my own regression on the factors, I can compare the magnitude and direction with existing research. </p>

<p>Anything related to this would be useful too; for example, if there is no paper which predicts the number of houses built, then something which gives, say, a utility yield for constructors would be helpful.</p>

CC BY-SA 3.0
2015-07-08T15:03:42.517Z
Is there a particular part of the pipeline that interests you? The pipeline is like: housing permits -> housing starts -> completions. There are a bunch of factors that affect new construction, and the long-term modeling (predicting future permits) is pretty difficult. In the short run, if you can take permits as a given, you'll find regional variation in construction timelines, that specific weather shocks add time to construction, and a higher rate of abandoned construction when the economy goes south.
dismalscience
CC BY-SA 3.0
2015-07-08T16:12:42.330Z
I'm interested in predicting future permits and on housing completions. It seems to me like housing completions is largely dependent on number of permits, so I'm probably more interested in the former: predicting future housing permits. Could you point me to the direction of literature in either of the two categories?
Alex
CC BY-SA 3.0
2015-07-08T16:36:02.583Z
I amend my previous comment; all of permits, starts, and completions would be good variables for me to study. Could you point me to the direction of literature in any/all of these categories?
Alex
<econometrics><reference-request><regression><housing>
Literature on factors affecting number of houses built
Alex
# Literature on factors affecting number of houses built

(Asked by: username_0 on 2015-07-08)

Is there any existing literature on the various factors affecting the number of houses constructed in a particular city over, say, a given month? Factors might include: quality of housing stock, a lagging value of housing sale price, house price to earnings ratio.

Preferably, I would like to see literature that describes the relative importance of these factors, perhaps by running a regression--so that way, when I do my own regression on the factors, I can compare the magnitude and direction with existing research.

Anything related to this would be useful too; for example, if there is no paper which predicts the number of houses built, then something which gives, say, a utility yield for constructors would be helpful.

**Tags:** econometrics, reference-request, regression, housing

> **Comments:**
> **username_1**: Is there a particular part of the pipeline that interests you? The pipeline is like: housing permits -> housing starts -> completions. There are a bunch of factors that affect new construction, and the long-term modeling (predicting future permits) is pretty difficult. In the short run, if you can take permits as a given, you'll find regional variation in construction timelines, that specific weather shocks add time to construction, and a higher rate of abandoned construction when the economy goes south.  
> **username_0**: I'm interested in predicting future permits and on housing completions. It seems to me like housing completions is largely dependent on number of permits, so I'm probably more interested in the former: predicting future housing permits. Could you point me to the direction of literature in either of the two categories?  
> **username_0**: I amend my previous comment; all of permits, starts, and completions would be good variables for me to study. Could you point me to the direction of literature in any/all of these categories?  

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## 1 answers

### Answer by username_2 on 2015-07-08. Score: 4

It won't help much with the high frequency variation of interest to you but this is a famous and important paper on this topic:

> I process satellite-generated data on terrain elevation and presence
> of water bodies to precisely estimate the amount of developable land
> in U.S. metropolitan areas. The data show that residential development
> is effectively curtailed by the presence of steep-sloped terrain. I
> also find that most areas in which housing supply is regarded as
> inelastic are severely land-constrained by their geography.
> Econometrically, supply elasticities can be well characterized as
> functions of both physical and regulatory constraints, which in turn
> are endogenous to prices and demographic growth. Geography is a key
> factor in the contemporaneous urban development of the United States.

The Geographic Determinants of Housing Supply (Saiz (2010))

This paper may also be useful:

> The long-run price elasticity for alternative specifications of new
> housing supply is estimated using U.S. annual data for 1950 through
> 1994. The basic model expresses residential construction as a linear function of new housing price and the prices of construction inputs.
> Long-run elasticities range from 1.6 to 3.7, suggesting that new
> housing supply is price elastic. Residential construction responds to
> both the real interest and expected inflation rates, but other
> construction cost variables perform poorly. However, the results are
> sensitive to the time-series processes underlying the variables. A
> modified model that expresses residential construction as a function
> of changes in input prices, rather than their levels, produces a
> long-run elasticity of about 0.8 and a significant inverse
> relationship between new housing supply and the construction wage
> rate.

The Long-Run Elasticity of New Housing Supply in the United States: Empirical Evidence for 1950 to 1994 ([Blackley (1999)](http://link.springer.com/article/10.1023/A:1007781228328))


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