Two Federal Reserve economists have developed a home price index which they claim operates in a more real time environment than those indices currently accepted as the industry standard.  Rather than base their index on sales prices, Elliot Anenberg and Steven Laufer have constructed theirs using a repeat-sales approach but relying on listing data.  Using Data on Seller Behavior to Forecast Short-run House Price Changes was published as part of the Fed's Financial and Economics Discussion Series.

The paper notes that home price changes have consequences for the economy as they affect both household wealth and an owner's ability to borrow.  But information on those prices, unlike that of other assets such as stocks, are reported with a significant time lag.  The Case-Shiller house price index contains information from several months earlier but has an immediate effect on home building related stock prices and it is likely that the prices indexes also have an impact on individual homeowners, policy makers, lenders, and others.  By using information that was available at the time of the contract negotiations, the authors designed their list-price index to mitigate this information friction.

The authors attribute the time lag in producing the traditional indices to a lack of incentive for buyer or seller to publicize the price once they have negotiated an agreement.  Even once the price is disclosed at closing there is typically another lengthy delay before the public record becomes available.  In contrast, before the contract is signed the seller has a strong incentive to broadcast the current asking price as a marketing tool.  Thus information on listing prices is disseminated through platforms such the Multiple Listing Services (MLS) in real time.  Once a sale agreement is reached those listings are removed.  The authors theorize that using the information on home prices before those homes are delisted could allow them to learn about the level of sale prices much earlier than what is currently available.  

The authors developed a new house price index reproducing the Case-Shiller repeat-sales index, substituting sales prices with an estimate based on the final list prices of all homes that are delisted.  Key to the methodology is associating each delisting with the most recent prior sale of that property, creating a pair of observations analogous to a pair of repeat sales in the Case-Shiller and other similar indices.  This allows a timelier index of price trends while maintaining the most attractive feature of other indices; their ability to control for changes in the mix of homes sold over time by partialing out a house-specific effect from each price. 

Testing their theory was complicated because the sale-to-list price ratio varies, both in the cross section and across time and because many delistings are done for reasons other than a sale.  The authors found that some of these variations could be explained by other observable information about seller behavior such as the time on market (TOM) and the history of list price changes.  This information was used to adjust the final list price up or down. 

The index was tested by using micro data from three large metropolitan areas, Phoenix, Seattle, and Los Angeles, over the period 2008-2012.  They found their index could account for hetroskedastic errors (i.e. homes with a longer interval between sales should be downweighted because the likelihood of unobserved changes to house quality are higher).  It could also account for value weighting - that more valuable homes comprise a larger share of a real estate portfolio and thus their appreciation/depreciation rates should be given more weight.  The index also accurately forecasts the Case-Shiller index several months in advance, outperforms forecasting models that do not use listings data, and for the one metropolitan area in which data on futures contracts are available, outperforms the market's expectations as inferred from prices on Case-Shiller future contracts.

The second set of data was micro data on home listings with dates from which can be derived the TOM.  There is no data to indicate the reason for delisting or whether, if it were delisted because of a sale, no information on the terms of that sale.  The listing also includes the specific property address and some information on the home's characteristics.   Each home was linked by address to its previous sales record.

All three cities in the sample experienced significant declines in house prices during the beginning of the sample period, although the magnitude of the decline varied considerably across cities   The sample period also covers time during which the homebuyer tax credit was in effect and the 2012 beginning of price recovery in the cities, all three of which are covered by the Case-Shiller 20-City Index.  The authors identified nearly a million properties that were delisted during the sample period and which they could link to a previous transaction record. A majority of listings are delisted without a list price change. The median TOM is between one and two months. Many delistings are relisted soon after delisting: 20 percent of delistings are relisted within less than a month and 17 percent of are relisted between 2 and 6 months later. Many of these relistings may be due to sales agreements that fall through because a mortgage contingency fails or an inspection fails.

The authors derived two index models.  In the first, which they called the simple-list price index they used the same regression equation as Case-Shiller except for the months where that index was not yet available and they substituted sales prices with the final list prices of delistings that were expected to close in a month.  For the previous sale, they used the house price level calculated from the transaction data alone rather than re-estimating it using both transactions and listings data.

They found that, despite the extreme changes in housing market conditions over the sample period the sale-to-list price ratio fluctuated within a band of only several percent but that variation does appear to be correlated with the house price cycle; periods of rising prices tend to have high sales-to-list price ratios.  Another potential source of bias was the inclusion of all delistings rather than just those that led to sale.  Delistings that led to sales tended to have lower list prices and the magnitude of that price difference was negatively correlated with the house price cycle. This share is also volatile over time, with hotter markets being associated with a higher probability of sale, suggesting that including all delistings, rather than only the ones that result in sales, will bias the index due to selection.

On average, there is a delay of about six weeks between delisting and closing. The distribution of delays does not change much over time. This suggests that the assumption of a time-invariant distribution seems very reasonable, especially since the index is calculated as a moving average of the previous three months.

The second model, the adjusted list price index, attempted to eliminate problems with including all delistings by delivering predictions for how outcomes would vary by applying observable listing variables such as time on the market and the list price history. That model attempts to describe the behavior of a homeowner trying to sell her house. It had to take into account various factors that might influence the outcome such as the value the sellers place on not selling and staying in the home which may arise from factors such as employment opportunities or changes in the family's social or financial situation; time constraints such as the start of the school year or a closing date on a trade-up home purchase.

The authors considered the ability of each index to forecast the Case-Shiller HPI at various time horizons - i.e. the number of weeks from the date of the last observed listings data until the end of the month they were trying to forecast.  At longer horizons an increasing share of the sales are from properties which have not yet observed delistings.  However, even five months into the future they found their index still had significant predictive power which occurs become sore transactions take a significant amount of time to close and because the smoothing process causes sales that close in a given month to affect the index for the two subsequent months as well.

The adjusted list price Index performs well, even at 12 weeks.  Not surprisingly performance improves as more listings information about the month becomes available.  Even the Simple List-Price Index performs well although the adjusted index delivers improved performance of about 20 percent.

They authors stress that their sample period covers one of the most volatile time periods in housing history and Phoenix, one of the most volatile sub-markets.  The fact that our index performs so well during this time period gives us confidence that performance would be as good, or possibly even better, out of sample."