CoreLogic 10-city composite Pending Price Index reveals home price change would remain flat in October and November

A home price index (HPI) is a vital tool for understanding valuation and risk trends in real estate markets. CoreLogic offers both the CoreLogic HPI® and the CoreLogic Case-Shiller Index® (CSI). Both proprietary indexes are highly trusted, widely respected and accepted valuation benchmarks. Since it takes time to record transactions, collect data and produce indexes, there is always a lag between the availability of the data and the calculation of the HPI. On the other hand, a sales contract is generally signed before a sale is settled. Figure 1 shows the distribution of days between the day a contract is signed and the day the transaction is closed¹. It takes a little more than one month to close a transaction after a contract is signed. CoreLogic has developed a Pending Price Index, which is calculated using the contract date rather than the close date. A hedonic approach is used to build the Pending Price Index,2 which by design is a leading indicator of HPI since the majority of the pending sales are closed eventually.

In this analysis, we show how a 10-city composite Pending Price Index is correlated to, and more importantly, leads its corresponding 10-city composite HPI. The 10-city composite CoreLogic Pending Price Index is the aggregated Pending Price Index for 10 major metropolitan areas: Charlotte, Chicago, Dallas, Los Angeles, Miami, Minneapolis, Portland, San Diego, Seattle and Las Vegas³.

Figure 2 shows the correlation between the month-over-month percentage change of the 10-city composite HPI and the month-over-month percentage change of different lags for the 10-city composite Pending Price Index⁴. As the correlation table shows, the CoreLogic HPI has the highest correlation with the lag one and lag two of the Pending Price Index while the CSI I has the highest correlation with the lag one of the Pending Price Index. This indicates that the Pending Price Index leads the HPI by a couple of months and is consistent with what we observed from Figure 1 that it takes, on average, a little more than one month to close a transaction.

Figure 3 plots the 10-city composite Pending Price Index with the 10-city composite CoreLogic HPI and CSI. All three indexes are normalized so they each have an index value of 100 as of January 2006. As we can see from Figure 3, prior to the crisis, the Pending Price Index signaled a peak several months earlier than the HPI, with the Pending Price Index topping in June 2006, the CoreLogic HPI topping almost three months later in September 2006 and the CSI topping in October 2006. Similarly, the Pending Price Index signaled a price bottom one to two months earlier than the HPI – as the Pending Price Index bottomed in December 2011 while the CSI bottomed in January 2012 and the CoreLogic HPI bottomed in February 2012. It is clear that the Pending Price Index can detect turning points in the housing market one to two months earlier than HPI.

So what does this Pending Price Index forecast for the near-term housing market? Figure 4 shows the projected⁵ and actual year-over-year changes for the 10-city composite index. The projection – based on the Pending Price Index – gave a fairly accurate forecast of what actually happened. We can also see that the year-over-year change for most of 2016 had been around 7 percent and the projected appreciation would remain at the 6 and 7 percent range in October and November.

By accessing contract prices – which are typically available more than one month sooner than the settlement data – for homes that have not yet settled, the Pending Price Index does not necessarily predict the near-term home price, but instead uses additional information about what is currently happening in the housing market to forecast what will be reflected in the future home price. By leveraging the information in the Pending Price Index, investors and financial institutions can stay one step ahead and be alerted to what may happen in the housing market, allowing them to plan accordingly.

1. CoreLogic MLS data
2. For more information about hedonic price index, please see Stephen Malpezzi’s paper Hedonic Pricing Models.
3. The composite index the weighted average of indexes in these cities. The weight used here is the housing stock.
4. The correlation table is calculated based on indexes from January 2006 to August 2016.
5. The projection is one-period ahead of projection. For instance, the January 2016 HPI is projected by using the November 2015 and December 2015 Pending Price Indexes, and the February 2016 HPI is projected by using the January 2016 and December 2015 Pending Price Index, and so on.

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