SLIDE 1
Michael Sklarz Michael Sklarz Pr Predictiv edictive Methods Confer Methods Conference ence May 2010 May 2010
SLIDE 2 Nearly 25 years ago, Profe
Nearly 25 years ago, Professo sor No r Norm Miller and I w rm Miller and I wrote
a pape paper on predicting home pric r on predicting home prices using various market-base es using various market-based indicato indicators. rs.
I am happy to report that
I am happy to report that afte after all this time, it is now r all this time, it is now possible possible to do this for most loca to do this for most local U.S. housing markets. l U.S. housing markets.
History of Predicting Home Prices
SLIDE 3 Access to key data el
Access to key data elements incl ements including uding
- Richer, and more timely, sales data
Richer, and more timely, sales data
Listing Prices
Shadow inventory
Expired an Expired and Withdrawn d Withdrawn Listings Listings
- Months of Inventory Remaining
Months of Inventory Remaining
Days o
Mark rket et
- Active Listing Prices versus
Active Listing Prices versus Sales Prices and SP/LP Ratio Sales Prices and SP/LP Ratio
Improvement in Home Price Index methodologies ice Index methodologies
- Ability to do macro and micr
Ability to do macro and micro level home price forecasts
- level home price forecasts
Key Developments Enabling Better Predictions
SLIDE 4 Sales activity is the most basic leading indicator of Sales activity is the most basic leading indicator of home prices home prices
Higher prices Lower prices Price bottom Increasing sales Increasing sales Decreasing sales
Escondido CA
SLIDE 5 Note how Note how Sa San Diego n Diego si signifi gnificantly overshot antly overshot in in the most the most recent recent cycle cycle and and has has since corrected since corrected
Peaks have been about every 10-12 years
Housing market cycles have actually been quite orderly
Data Source: Moody’s/Economy.com
SLIDE 6 Plotted inversely
Sold Market Time is an excellent leading indicator
SLIDE 7 Months of Inventory Remaining is another powerful leading indicator which leads prices by anywhere from 6 to 18 months
Price Percent Change Months of Inventory
SLIDE 8 San Diego Median Price and Sold Market Time
Data Source: Moody’s/Economy.com and CAR
SLIDE 9 Sold Market Time Price Percent Change
These leading indicators work on all geographic levels and property types
SLIDE 10
Thousa sand Oa nd Oaks CA Sin ks CA Single le Fa Family Cu mily Curre rrent List t Listings ings a and Past Year Sa d Past Year Sales By Price/Livin les By Price/Living Area Area
Combining supply with demand data provides much more insight to the overall and internal conditions of a particular real estate market
High Liquidity Low Liquidity
SLIDE 11
Thousa sand Oa nd Oaks CA Sin ks CA Single le Fa Family mily
Ve Very weak ry weak ma marke rket Ab Above $350/S e $350/Sq Ft Ft
Months of Inventory Remaining can also be used to stratify market condition by price range in a particular market
SLIDE 12 New list price declining Sold prices flat, but declines shortly thereafter New list price increasing Sold Prices rise shortly thereafter
New Listing Price is the best measure of the current direction of home prices – widely followed HPIs are typically reported with a two month lag and really reflect prices from 3 to 4 months ago
SLIDE 13 Sign Significan ificant Var Varian ances
There is a definite need for micro-market data and HPIs
SLIDE 14
REO and Foreclosure sales have a significant and diverse impact in many markets
Sacramento Single Family REO Discount
SLIDE 15
It is possible to create very granular REO discount factors
SLIDE 16 Defining and identifying home price bubbles
150% increase
rule
SLIDE 17
SLIDE 18 Data Source: Lewtan ABSNet HomeVal
decline declines in in home prices home prices lea lead t to g grea eater ter delinquencie delinquencies and and default default
Home price declines drive mortgage delinquency and default – start with state level HPIs for current home values
SLIDE 19
Data Source: Lewtan ABSNet HomeVal
Better measures of home prices lead to better predictions of mortgage delinquency and default – CBSA level HPIs for current home values
SLIDE 20
Better measures of home prices Better measures of home prices lead to better predictions of lead to better predictions of mortgage delinquency and default – mortgage delinquency and default – Zip level HPIs ip level HPIs fo for current r current home home values values
Data Source: Lewtan ABSNet HomeVal
SLIDE 21
Using Address Matchi Using Address Matching and AVMs to cal ng and AVMs to calcul ulate indivi ate individual home dual home values do values do the the best job fo best job for predicting mo r predicting mortgage rtgage delinquency and delinquency and default default
Data Source: Lewtan ABSNet HomeVal
SLIDE 22
SLIDE 23 Original LTV Distribution of Active Loans
500 1000 1500 2000 2500
2 4 6 8 1 1 2 1 4 1 6 1 8 2 2 2 2 4 2 6 2 8 3 3 2 3 4 3 6 3 8 4
LTV Range in Percent N u m b e r o f L
n s
The significant move to the right, with higher LTV’s Is not good
Data Source: Lewtan ABSNet HomeVal
GSAMP 2007-NC1 Original and Current LTV Distributions of Active Loans
SLIDE 24 A typical RMBS is quite complicat A typical RMBS is quite complicated - ed - note the wide variati
- te the wide variation in
- n in
constituent home price levels constituent home price levels and performances prior to and and performances prior to and afte after o r origination rigination
- 4754 Zip Codes Originally
- 3236 Zip Codes Currently
It should not have been that difficult to see that late 2006 was a bad time to be
- riginating risky mortgages
SLIDE 25 Data Source: Lewtan ABSNet HomeVal
Very ba ry bad ti d time to to
ate risky e risky secu securitie rities
SLIDE 26
Relationship between declining home prices and default is clear
Data Source: Lewtan ABSNet HomeVal
