The need for real time residential real estate data has never been greater. Yet, the primary US metro benchmarks, the S&P/Case-Shiller (CS) Home Price Indices, will finally update with December numbers on Tuesday, February 28th.
Parcl Labs forecasted December updates based on CS10 methodology and we expect continued price declines in 8 of 10 markets.
We predict Boston (0%) will record flat MoM price changes and San Fransisco (.5%) will be the lone market to observe a price increase.
Las Vegas, Los Angeles, and New York will see a price decline of 0.5% or less. This result represents a deceleration in price declines in Vegas and Los Angeles compared to the November results.
Chicago, Denver, Miami, San Diego, and DC will see more price declines of 1% or lower. The news is particularly bad for Denver (-2.9%) and Miami (-2.1%). We predict that both markets will be down over 2%, which represents a 3 times greater price decline compared to November.
Real estate prices are increasingly volatile and dynamic based on local market factors. Parcl Labs provides data and analysis you can trust - our November Predictions were directionally consistent for 8/10 markets and within 70 basis points for those 8 markets.
Timely Real Estate Prices Matter
The need for real time residential real estate data has never been greater. On Tuesday, February 28 at 9:00am EST the S&P/Case-Shiller Home Price Indices will update with December numbers.
We at Parcl Labs didn’t feel like waiting to see what’s happening in housing markets across the US. We used our real time Parcl Labs data, reconstructed what we could glean from the opaqueCase Shiller methodology, and predicted the numbers that will be reported for all Case Shiller 10 metro areas for November (scheduled to be reported on February 28th). This report gives us insight into how markets are evolving for single family, repeated sales homes that fall outside the definition of home flipping (turnover time of less than 6 months) during what continues to be one of the most volatile periods real estate has ever experienced.
December Case Shiller Market Highlights
Stabilizing December Markets
San Francisco is the only market to record a MoM price increase, up 0.5% since November.
Boston will observe no change in price per square foot from November to December.
Las Vegas's price declines will decelerate in December, down just 0.5% MoM.
Los Angeles will see a 0.3% decline, suggesting a December stabilization in the market despite a slight decrease.
New York (-0.1%) will remain stable with no significant change in price per square foot from November to December.
Declining December Markets
Chicago will experience a 1.2% decrease in price per square foot from November to December.
Denver will observe the largest price declines on a percentage basis, down nearly 3% MoM.
Miami sees the second largest drop in housing prices (-2.1%), indicating a December cooling off within this market.
San Diego observes a moderate 1.1% decrease in price per square foot, decelerating its rate of decline slightly since November.
Washington DC is the worst-performing East Coast market in December, down -1.6% MoM.
Check out our market breakdowns to get detailed information on Case-Shiller predictions and the current state of housing within each of these major markets.
We carefully track our estimates against published numbers. Outlined below is our error rates for 2022 for the CS-10 metro areas. We continue to refine our approach based on our past performance and increased understanding of the Case Shiller methodology.
Applying these error rates to our December estimates gives us a range of possible values we feel the Case Shiller indices will fall within for each market:
Parcl Labs October Prediction Performance
Parcl Labs predictions were directionally consistent for 8/10 markets.
For 8 out of 10 markets, our predictions were within 70 basis points.
For 2 out of 10 markets we were off by ~1.75%, Los Angeles and San Francisco
Overall average delta: 0.67%
Subscribe to our newsletter
Jason leads the data team at Parcl Labs. Jason brings his experience from Microsoft and Deloitte where he worked on international data standardization and machine learning problems at scale.