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 November numbers on Tuesday, January 31st.
Parcl Labs forecasted November updates based on CS10 methodology and we expect continued price declines in 8 of 10 markets. However, price declines will flatten, with half of the declining markets seeing price drops of .3% or less.
The most dramatic month over month (MoM) price decreases are recorded in Los Angeles (-2.5%), Las Vegas (-2.2%), and and San Diego (-1.0%).
New York and San Fransisco will observe slight price increases, up .1% and .2% respectively. These positive results hide more recent price volatility in both of these markets.
Real estate prices are increasingly volatile and dynamic based on local market factors. Parcl Labs provides data and analysis you can trust — our October Predictions were directionally consistent for 10/10 markets, within 10 basis points for 4/10 markets, and within 50 basis points for 7/10 markets.
Timely Real Estate Prices Matter
The need for real time residential real estate data has never been greater. On Tuesday, January 31 at 9:00am EST the S&P/Case-Shiller Home Price Indices will update with November 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 January 31). 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.
November Case Shiller Market Highlights
8 of 10 CS-10 markets will witness price decreases for November. The 8 declining markets are Boston, Chicago, Denver, Las Vegas, Los Angeles, Miami, San Diego, and Washington DC.
However, the rate of price declines will steady somewhat compared to October readings. Boston, Chicago, Denver, and Miami will record price declines equal to or less than -.3%.
The worst performing November markets will be Los Angeles (-2.5%), Las Vegas (-2.2%), and San Diego (-1.0%). This result is further evidence for the West Coast’s negative regional divergence in real estate prices. Read Parcl Labs Q4 Real Estate Report to learn more about how West markets have cooled much much more significantly compared to Southeastern Coast counterparts.
New York will be a relative bright spot in the November CS results, up .1% MoM. At Parcl Labs, we believe this positive November result is misleading when considering the current overall health of New York real estate. The Case-Shiller methodology does not account condos or new construction. Parcl Labs Q4 Real Estate Report found condos are down 11% Qtr/Qtr and newer homes are down 33% Qtr/Qtr in New York.
San Francisco will record the best November results, up .2% MoM.
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 November 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 10/10 markets.
For 4 out of 10 markets, our predictions were within 10 basis points. Our Las Vegas and Boston predictions were near perfect, with deltas of .01% and .02% respectively.
For 7 out of 10 markets, our predictions were within 50 basis points.
Denver and Washington DC were our worst October predictions, with deltas of -1.08% and -1.39% respectively
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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.