U.S. Housing Starts: October 2015 Preview

Nov. 17, 2015 1:14 PM ETIYR, XHB, ITB, PKB
James Picerno profile picture
James Picerno

Housing starts are expected to decline to 1.168 million units (seasonally adjusted annual rate) in Wednesday's October update, according to The Capital Spectator's average point forecast of several econometric estimates. The projection represents a modest retreat from 1.206 million units in the previous month's report for residential construction activity.

The Capital Spectator's average forecast is near the middle of three estimates based on recent surveys of economists.

Here's a closer look at the numbers, followed by brief definitions of the methodologies behind The Capital Spectator's forecasts that are used to calculate the average estimate:


VAR-3: A vector autoregression model that analyzes three economic series to project housing starts: new home sales, newly issued permits for residential construction, and the monthly supply of homes for sale. VAR analyzes the interdependent relationships of these series with housing starts through history. The forecasts are run in R using the "vars" package.

ARIMA: An autoregressive integrated moving average model that analyzes the historical record of housing starts in R via the "forecast" package.

ES: An exponential smoothing model that analyzes the historical record of housing starts in R via the "forecast" package.

R-1: A linear regression model that analyzes the NAHB Housing Market Index in context with housing starts. The historical relationship between the data sets is applied to the more recently updated NAHB Housing Market Index to project housing starts. The computations are run in R.

TRI: A model that's based on combining point forecasts, along with the upper and lower prediction intervals (at the 95% confidence level), via a technique known as triangular distributions. The basic procedure: 1) run a Monte Carlo simulation on the combined forecasts and generate 1 million data points on each forecast series to estimate a triangular distribution, 2) take random samples from each of the simulated data sets and use the

This article was written by

James Picerno profile picture
James Picerno is a financial journalist who has been writing about finance and investment theory for more than twenty years. He writes for trade magazines read by financial professionals and financial advisers. Over the years, he’s written for the Wall Street Journal, Barron’s, Bloomberg Markets, Mutual Funds, Modern Maturity, Investment Advisor, Reuters, and his popular finance blog, The CapitalSpectator. Visit: The Capital Spectator (www.capitalspectator.com)

Recommended For You


To ensure this doesn’t happen in the future, please enable Javascript and cookies in your browser.
Is this happening to you frequently? Please report it on our feedback forum.
If you have an ad-blocker enabled you may be blocked from proceeding. Please disable your ad-blocker and refresh.