A billion prices

Econbrowser has an interesting article on the Billion Prices Project, which looks for daily price movements on items across the web. This yields a price index that’s free of quality change assumptions, unlike hedonic CPI measures, but introduces some additional issues due to the lack of control over the changing portfolio of measured items.

A couple of years ago we built the analytics behind the RPX index of residential real estate prices, and grappled with many of the same problems. The competition was the CSI – the Case-Shiller indes, which uses the repeat-sales method. With that approach, every house serves as its own control, so changes in neighborhoods or other quality aspects wash out. However, the clever statistical control introduces some additional problems. First, it reduces the sample of viable data points, necessitating a 3x longer reporting lag. Second, the processing steps reduce transparency. Third, one step in particular involves downweighting of homes with (possibly implausibly) large price movements, which may have the side effect of reducing sensitivity to real extreme events. Fourth, users may want to see effects of a changing sales portfolio.

For the RPX, we chose instead a triple power law estimate, ignoring quality and mix issues entirely. The TPL is basically a robust measure of the central tendency of prices. It’s not too different from the median, except that it provides some diagnostics of data quality issues from the distribution of the tails. The payoff is a much more responsive index, which can be reported daily with a short lag. We spent a lot of time comparing the RPX to the CSI, and found that, while changes in quality and mix of sales could matter in principle, in practice the two approaches yield essentially the same answer, even over periods of years. My (biased)  inclination, therefore, is to prefer the RPX approach. Your mileage may vary.

One interesting learning for me from the RPX project was that traders don’t want models. We went in thinking that sophisticated dynamics coupled to data would be a winner. Maybe it is a winner, but people want their own sophisticated dynamics. They wanted us to provide only a datastream that maximized robustness and transparency, and minimized lag. Those are sensible design principles. But I have to wonder whether a little dynamic insight would have been useful as well since, after all, many data consumers evidently did not have an adequate model of the housing market.

The RPX is up

While the Case-Shiller index is down and the conventional wisdom suggests that housing prices will continue to fall, the RPX composite is up for the first time since 2007. The year-on-year ratio hit bottom in Feb 09. The RPX has a lot less lag than the CSI, but also a seasonal signal, so this could merely mean that seasonally adjusted prices are just falling more slowly, but it would be nice if it reflected green shoots. I’m not holding my breath though.

Real Estate Roundup

Ira Artman takes a look at residential real estate price indices – S&P/Case-Shiller (CSI), OFHEO, and RPX. The RPX comes out on top, for (marginally) better correlation with foreclosures and, more importantly, a much shorter reporting lag than CSI. This is a cause for minor rejoicing, as we at Ventana helped create the RPX and are affiliated with Radar Logic. Perhaps more importantly, rumor has it that there’s more trading volume on RPX.

In spite of the lag it introduces, the CSI repeat sales regression is apparently sexy to economists. Calculated Risk has been using it to follow developments in prices and price/rent ratios. Econbrowser today looks at the market bottom, as predicted by CSI forward contracts on CME. You can find similar forward curves in Radar’s monthly analysis. As of today, both RPX and CSI futures put the bottom of the market in Nov/Dec 2010, another 15% below current prices. Interestingly, the RPX forward curve looks a little more pessimistic than CSI – an arbitrage opportunity, if you can find the liquidity.

Artman notes that somehow the Fed, in its flow of funds reporting, missed most of the housing decline until after the election.