Release Date: July 23, 2009 May 2009

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1 Release Date: July 23, 2009 May 2009 Key Characteristics The RPX is designed to be a daily indication of the spot price for residential real estate and, as such, may provide an early view of trends in the broader economy. We have been observing strength in the RPX since April, and it now appears that this improvement in home prices was an early indicator of some strength in the general economy. Twenty-two of the 25 metropolitan statistical areas (MSAs) tracked by Radar Logic displayed month-overmonth price increases in May The Exceptions were Atlanta, Las Vegas and New York. Prices in most of these MSAs increased more than would be expected given historical seasonal patterns. This contrasts starkly to the month-over-month price changes in May 2008, when the seasonal strength typically observable in spring and summer was largely absent. The increase in home prices from April to May 2009 was larger than the average increase for that time of year. This could indicate that seasonal price fluctuations do not fully account for the strength we are seeing in many areas and that seasonal gains are being augmented by a more general recovery in the housing market. Unusually mild price declines in the coming autumn and winter would provide further evidence that some markets have started to recover. In contrast to the price growth displayed by most of the MSAs we track, prices in Atlanta, Las Vegas and New York declined on a month-over-month basis in May. The price declines in New York and Las Vegas are not surprising given the reliance of their local economies on industries that have been hit hard during the current recession: finance in the case of New York and tourism in the case of Las Vegas. The absence of seasonal strength in Atlanta is not unusual either, as seasonal factors do not have a particularly strong influence on the Atlanta RPX relative to the influence of seasonality on other MSAs. Still, May s decline indicates that the recovery that appears to be starting elsewhere in the country has not yet begun in Atlanta. The 25-MSA Composite has increased 3.7% since March 30, when it hit its lowest point since the beginning of the housing crisis. Home prices in the western region of the United States have performed particularly well recently, increasing by 6.9% since hitting their low on January 22. Price fixings for RPX forward contracts written on the 25-MSA Composite indicate that RPX investors are significantly more bullish currently than they were a quarter ago. Fixings for contracts with maturities in 2009 and 2010 are 23% and 24% higher than they were in April of this year. We expect the S&P/Case-Shiller 10-City and 20-City composites for May 2009 to remain at their April levels, roughly 150 and 139, respectively.

2 Discussion In a report released earlier this month, we suggested that the RPX could be a leading indicator of economic fluctuations beyond the housing sector. The premise was that home prices are closely tied to consumer sentiment in the current economic environment because housing accounts for a large share of consumer wealth and, after the sharp declines in home prices over the last few years, consumers are not likely to take the value of their homes for granted. As an indication of the daily spot value of residential real estate, the RPX provides an early view of home price dynamics that can ripple out into the broader economy. In most markets, home sales and home prices are seasonal insofar as they show relative strength in spring and summer and relative weakness in autumn and winter. In 2008, the seasonal strength typically observable in spring and summer was largely absent. Whereas the 25-MSA RPX Composite has traditionally increased each month from February through June, in 2008 it decreased throughout that period. The only indication of seasonal strength in the composite last year was a reduction in the rate its decline from -2.5% in January to -0.2% in May. By the beginning of autumn 2008, the rate of decline had accelerated back to -3.1%. As of May 2009, however, seasonal strength has returned to many housing markets. Twenty-two of the 25 MSAs tracked by Radar Logic displayed month-over-month price increases in May In fact, prices in most of these metropolitan areas increased more than would be expected given historical seasonal patterns (see the discussion below for details). Given the stronger-than-average price growth from April to May, we will watch price movements in the autumn and winter with interest to see if prices decrease less than would be expected given past declines during those times of year. Below-average price declines in autumn and winter would provide further evidence that housing markets have turned a corner and begun the process of recovery.

3 Exhibit 1 charts the the change in the 25-MSA RPX Composite between April and May 2009 and compares it to the average change between April and May from 2000 to 2008 and the average change between April and May from 2000 to The period from 2000 to 2008 includes both boom and bust years, while 2000 to 2006 corresponds roughly to the housing boom. The increase in the Composite in May 2009 was larger than the average gains during the month of May over both these periods. The premium to the average gain during May could indicate that seasonality alone does not fully account for the strength we are seeing in the Composite, and that seasonal market forces in many areas are being augmented by a more general recovery in the housing market. Exhibit 1 also shows the April-to-May change of a composite index based on the nine western MSAs tracked by Radar Logic. Clearly, the price gains in the West during May 2009 outstrip the average gains for that time of year, even when only considering the housing boom years from 2000 to The western MSAs were among the first to decline at the beginning of the housing bust, and it appears they are now among the first to show signs of recovery. Exhibit 1

4 While seasonal strength is returning to the 25-MSA Composite, the same cannot be said of the RPX for the New York MSA. This can be seen in Exhibit 2, which shows the change in these indices (as well as the western region composite) from December to the following May and from May to the following December for each year from 2000 to Positive changes are displayed with a green background, while negative changes are displayed with a red background. If the price change in a given year is greater (less) than the price change over the same period a year earlier, the change is presented in green (red) text. While the December-to-May change in the 25- MSA Composite improved from -6.1% in 2008 to -1.5% in 2009, the December-to-May decline in the New York RPX accelerated from -1.1% in 2008 to -6.6% in Bearing in mind that the New York RPX makes up 23.1% of the Composite, it is apparent that, were New York not suffering so badly, the Composite would have performed even better. Exhibit 2 25 MSA Composite % Price Change Dec to May % Price Change May to Dec Western Region Composite % Price Change Dec to May % Price Change May to Dec % 5.1% % 5.8% % 2.0% % 0.0% % 6.5% % 4.5% % 6.6% % 6.9% % 6.2% % 5.4% % 4.2% % 3.8% % 3.4% % 3.1% % 9.5% % 12.9% % 16.7% % 21.1% % % % Price Change Dec to May New York % Price Change May to Dec % 4.8% % 4.3% % 10.3% % 7.4% % 8.0% % 5.6% % 1.5% % 3.5% % 11.2% %

5 Exhibit 3 shows that the flattening of the 25-MSA Composite, which was discussed in previous reports, has now become a month-over-month increase of 2.1%. This is in stark contrast to the same period during 2008, when a decrease in the velocity of home price depreciation gave way to the worst loss in housing value in recent history. The RPX Composite has increased 3.7% since its trough on March 30, which is impressive considering that the long-term annual growth rate of housing prices is roughly 3%. 1 Exhibit 3 1 The long-term annual growth rate was calculated using historical values for the S&P/Case-Shiller 10-City Composite dating back to 1987.

6 The western region of the United States has been particularly hard hit by the housing crisis, both in terms of home price depreciation and foreclosure activity. Exhibit 4 shows that the western MSAs are now ticking up, in large part due to an influx of buyers chasing bargains in the California markets. The western region price began to stabilize precisely when it reached the point where it would have been had it grown at its historical growth rate since It appears that home prices in the western region have shed the excess value they accrued during the housing bubble and are now returning to something similar to their longterm growth trajectory. Of course, as we mentioned above, home prices are seasonal, so we expect the home prices in the west to drop below their long-term trajectory in the fall. Next spring, when prices strengthen once again, we could see prices in the West begin to oscillate above and below the extrapolation line of the historical annual growth rate. Exhibit 4 1 The Western Region RPX Composite is derived from RPX values for Denver, Las Vegas, Los Angeles, Phoenix, Sacramento, San Francisco, San Diego, San Jose and Seattle. 2 The pre-boom appreciation rate was derived using historical values for the S&P Case-Shiller indices for Los Angeles, San Diego, San Fracisco and Seattle dating back to 1991.

7 RPX forward contracts provide a means of gauging the view of housing from an institutional investor perspective. As can be seen from Exhibit 5, forward fixings for the 25-MSA Composite indicate that RPX investors are significantly more bullish currently than they were a quarter ago and 2010 fixings are trading 23% and 24% higher than they were in April of this year. This trend may indicate investor sentiment moving from a bearish view to a more cautious and in some cases bullish stance on the housing market. Despite the fact that fixings are still below the current RPX spot price, which indicates that investors still expect prices to decline, the narrowing gap parallels signs of stability in the RPX. Exhibit 5

8 May 2009 S&P/Case-Shiller Composite Home Price Indices In the RPX Housing Market Report for April 2009, which we released June 25, we used our Daily Prices to predict the S&P/Case-Shiller 10-City and 20-City composite home price indices for April. We predicted the 10- City index would be approximately 151, and the 20-City index would be about 140. The actual values, released June 29, were and , respectively. This month, we expect the S&P/Case-Shiller composites to remain more-or-less stable at their April levels. The May City composite will be roughly 150 and the 20-City composite to be roughly 139. On June 30, two new investment products with values tied to the S&P Case-Shiller 10-City composite started trading on the New York Stock Exchange: MacroShares Major Metro Housing Up (UMM) and MacroShares Major Metro Housing Down (DMM). They each have a pool of assets invested in short-term Treasury bills and overnight repurchase agreements. Assets shift back and forth between the two trusts to reflect changes in the home price index. At present, UMM has about $9 million in assets, while DMM has a little more than $14.5 million. Given our expectation that the 10-City composite will stabilize, investors will want to watch for signs of bullishness in UMM and DMM prices when the new S&P/Case-Shiller index values are announced on July 28. Blitz Report on Real Estate Markets In the latest Blitz Report on Real Estate Markets, released July 13, economist Steve Blitz argues against the notion that home prices are not going to recover on a national scale until the economy is growing and unemployment is falling. He notes that changes in the labor market over the last two decades have made job growth an increasingly less reliable indicator of whether the economy is turning up, and points out home prices have recovered before the end of each recession since Home prices are, in truth, rising before consumers perceive the recession has ended and well before the employment rate starts to turn down, says Blitz. He predicts that home prices will recover in the coming months, and that 1-year average RPX prices will gain 3.3% in 2010, 6.5% in 2011 and 10% in Compass Corner, by Rob Kessel, Managing Partner of Compass Analytics In last month s Compass Corner, we introduced the concepts behind how housing values and housing value forecasts ( HPAs ) impact the valuation of mortgage servicing rights ( MSRs ). By reviewing a traditional cash flow function and mechanics of mortgage servicing, we pointed out several cash flow components that are impacted by HPA, namely prepayment speeds and servicer advances/expenses associated with non-performing loans. We discussed the challenges modelers face in formulating and translating assumptions into model adjustments to appropriately reflect realistic impact of HPA in models. And finally, we promised in this month s Compass Corner to try our hand at doing just that and providing some measures of HPA sensitivity in the process. We start with HPA impact on prepayment speeds and consequently MSR valuations. First, let s differentiate between voluntary and involuntary prepayments. Voluntary prepayments are refinances and home sales, involuntary prepayments are defaults leading to foreclosure. For this exercise, we ll assume that voluntary prepayments increase as HPA assumptions are relatively more positive than baseline, i.e. house values are expected to rise more than predicted, prompting more housing turnover. We ll also assume involuntary prepayments rise as property values decline relative to baseline, leading to less or negative equity in property values. As a corollary to HPA-induced defaults (and involuntary prepayments), we expect servicers expenses to

9 rise as loans go into default. Servicers must make advances to the investor, taxing authority and insurance company, make calls to delinquent borrowers, implement loan modification efforts, initiate legal proceedings and ultimate manage property disposition. As noted last month, model assumptions bake in baseline assumptions about HPA prepayments so any HPA impact to MSR values will be due to HPA forecasts different than those already incorporated in baseline assumptions. Turning from the business logic to implementing model adjustments, let s try the following: Prepayments speeds, expressed as CPR for annual prepayment rate and SMM for single monthly rates might be adjusted as follows: HPA-Adjusted SMM(i) (Voluntary) = Original SMM(i) (Voluntary) + HPA Increase(i) where i represents the month of the future payment and HPA Increase would be the percentage the new HPA Forecast exceeds the original HPA forecast. In addition, we put a 4% SMM floor in place. See Table I, which illustrates the change in prepayment speeds this simplistic model would imply if HPA forecasts improved: Delinquency and Default rates are also expressed in percentages of the portfolio, and here our modeling would be more realistic by considering how HPA changes impact the future LTV, or equity, a borrower has in his or her home. However, to keep things simple for this exercise, we ll express HPA impact on delinquency as follows: HPA-Adjusted Delinquency Rate (i,d) = Original Delinquency Rate (i,d) *((HPA Increase(i)/20)-1) where d refers to the delinquency status, including 30-Day, 60-Day, 90-Day, 120-Day and Foreclosure and i represents the month of the payment again. In this oversimplified formula, delinquency rates would be cut by 50% for HPA forecasts 10% over baseline and delinquency rates would increase by 50% in 10% HPD scenarios. See Exhibit 6 for more examples. Exhibit 6 Hypothetical HPA Impacts on SMM and Delinquency/Default Rates Months HPA Increase Baseline SMM Adjusted SMM Baseline Delq Adjusted Delq So what impact will different HPA forecasts if we employ our hypothetical models discussed above? Using CompassPoint TM s MSR model, new agency production and otherwise standard industry assumptions, we implemented the simplistic assumptions above and derived the following MSR value (price in %) sensitivity to HPA, first considering prepayments alone and then considering HPA impact on prepayments and delinquency and default behavior. Exhibit 7 summarizes the results: Exhibit 7 Sample HPA Impact on MSR Prices (%) HPA Scenario 1) SMM Alone 2) SMM w/delq. 2) % Change % % Unchanged % %

10 Interpreting the results above demonstrate that when HPA deviates from baseline assumptions, MSR values can change appreciably. With our simple assumptions, the results indicated that HPA impact to voluntary prepayments significantly outweighed any credit impact. However, these results are dependent on the collateral modeled in this case agency loans. Whereas we oversimplified HPA/Prepayment and Delinquency relationships in this exercise, it is incumbent upon analysts to translate changes from HPA baselines into meaningful model adjustments that appropriately modify prepayment and delinquency assumption given their portfolio composition and the current marketplace. This analysis would necessarily disaggregate a portfolio by product type, performance and MSA and other variables to express HPA exposure on a more granular basis. Analysts will need to run HPA scenario analysis frequently as HPA and market conditions change and regularly update their portfolios loans current property values so as to more accurately derive a future LTV moving forward, a task well suited for Radar Logic Daily Prices. Another goal will be to unearth what HPA assumptions are already built into the prepayment and delinquency models employed by the analyst so changes from baseline assumptions may be more explicitly identified. Finally, once MSR HPA sensitivity is confidently modeled and disaggregated, MSR investors can contemplate hedging MSR HPA risk with derivatives such as RPX derivatives, using their modeled MSR HPA sensitivity to derive appropriate hedge notional values. Developments in the Housing Market The following calendar contains upcoming economic and housing market data releases in July and August Housing Release Calendar Release Release Date The University of Michigan and Thomson Reuters: Consumer Sentiment Report July 24, 2009 U.S. Bureau of the Census: New Home Sales July 27, 2009 S&P Case-Shiller Home Price Index July 28, 2009 U.S. Department of Commerce: GDP Figures July 31, 2009 National Association of Realtors: Pending Home Sales Index August 4, 2009 U.S. Department of Labor: Employment Situation August 7, 2009 U.S. Bureau of the Census: Retail Sales August 14,2009 Bureau of Labor Statistics: Consumer Price Index August 15, 2009 U.S. Bureau of the Census: Housing Starts August 17, 2009 National Association of Realtors: Existing Home Sales August 23, 2009 Past recessions exposed the fragmented nature of the economy insofar as market participants have been able to observe different parts of the economy rebounding independently. As the economy recovers from the present situation, we are likely to observe housing emerge as one of the first sectors to stabilize and regain traction. There have been several positive reports about the housing market in June and July. The S&P/Case-Shiller HPI release on June 30th echoed our observation of stability in the RPX. In contrast to the May report, which stated that we see no evidence that a recovery in home prices has begun, Standard and Poor s latest release reported that the Case-Shiller HPI has shown improvement in annual returns. In the same release David M. Blitzer, Chairman of the Index Committee at Standard and Poor s was quoted as saying, it seems some stabilization may be appearing in some of the regions. The National Association of Realtors Pending Home Sales Index release on July 1st also showed a slight uptick, moving from 90.3 to 90.7 month over month. Although stability and recovery are not far off, murmurs from the housing bust should continue to affect the dynamics of the housing market. If banks continue the slow pace of foreclosures due to political pressure, the

11 inventory of foreclosures could remain for years. While this should not prevent a recovery, it may change the assumptions that builders and existing home sellers have traditionally relied on to sell inventory. Increasingly, homebuilders are finding that they need to compete directly with banks looking to unload REO inventory. For example, DR Horton adopted a strategy where they dropped prices in Las Vegas as much as 50 percent in order to lure buyers looking for bargains at the motivated price point. The question that remains is whether builders will be able to remain profitable if they must shrink their margins to compete with REO sales. Builders may have to focus on smaller and cheaper construction projects in order to remain competitive. According to the U.S. Census Bureau, privately-owned housing starts increased 3.6% on a seasonally adjusted basis from May to June, after having decreased 12.8% from April to May.

12 Exhibit 8: 25 Metropolitan Statistical Areas (MSAs, Ranked by 1-Year % Change) May 2009 Rank Apr 2009 Rank MSA PPSF May 2009 vs. May 2008 May 2008 vs. May 2007 May 2009 vs. Apr 2009 May 2008 vs. Apr Milwaukee, WI $ % 2.1% 4.9% 4.1% 2 2 Philadelphia, PA $ % -8.0% 1.8% 1.2% 3 6 Charlotte, NC $ % -0.5% 4.7% 0.8% 4 3 Columbus, OH $ % -0.1% 3.2% 2.6% 5 4 St. Louis, MO $ % -7.2% 3.3% 2.9% 6 7 Cleveland, OH $ % -7.0% 4.0% 0.8% 7 5 Denver, CO $ % -7.6% 2.3% 1.7% 8 8 Boston, MA $ % -10.0% 4.6% 5.2% 9 10 Washington, DC $ % -10.7% 3.7% 0.8% Seattle, WA $ % -4.8% 2.8% 0.7% San Diego, CA $ % -27.9% 3.5% -2.3% Los Angeles, CA $ % -23.7% 3.7% -2.0% 13 9 New York, NY $ % -4.6% -1.7% 0.7% Minneapolis, MN $ % -8.6% 5.5% -1.8% Jacksonville, FL $ % -7.9% 0.8% 1.9% Chicago, IL $ % -9.1% 0.2% -1.6% Tampa, FL $ % -17.2% 0.5% 1.8% Atlanta, GA $ % -5.9% -0.2% 0.1% Sacramento, CA $ % -30.2% 2.7% 1.5% Detroit, MI $ % -11.6% 1.0% 2.6% San Jose, CA $ % -13.8% 4.7% 1.4% San Francisco, CA $ % -21.6% 7.3% -1.9% Miami, FL $ % -22.5% 2.0% -0.9% Phoenix, AZ $ % -25.4% 2.4% -2.1% Las Vegas, NV $ % -29.9% -0.6% -3.5% Manhattan Condo 2 $ % 8.2% -18.2% 2.6% Source: 28-Day RPX value for each MSA as of 5/21/2009 = positive = neutral = negative 1 Historical prices used to calculate changes in St. Louis include late-arriving data not included in published series 2 Manhattan Condo is a subset of the New York MSA Exhibit 9: Metro Areas Ranked by 2-Year and 5-Year Annualized Change Leading 5 Metro Areas (2-Year Annualized % Change) Trailing 5 Metro Areas (2-Year Annualized % Change) Rank MSA % Change Rank MSA % Change 1 Milwaukee, WI 1.5% 1 Las Vegas, NV -32.3% 2 Charlotte, NC -3.3% 2 Phoenix, AZ -28.2% 3 Columbus, OH -3.4% 3 Sacramento, CA -25.7% 4 Philadelphia, PA -6.0% 4 Miami, FL -24.1% 5 Cleveland, OH -7.6% 5 San Francisco, CA -23.5% Leading 5 Metro Areas (5-Year Annualized % Change) Trailing 5 Metro Areas (5-Year Annualized % Change) Rank MSA % Change Rank MSA % Change 1 Milwaukee, WI 4.0% 1 Las Vegas, NV -11.0% 2 Seattle, WA 3.7% 2 Detroit, MI -9.5% 3 Philadelphia, PA 3.7% 3 Sacramento, CA -8.8% 4 Charlotte, NC 1.2% 4 San Diego, CA -8.4% 5 New York, NY 1.1% 5 San Francisco, CA -5.0% Source: 28-Day RPX analytics as of 5/21/2009

13 Exhibit 10: Transaction Counts 1 MSA May 2009 vs. May 2008 May 2008 vs. May 2007 May 2009 vs. Apr 2009 May 2008 vs. Apr 2008 San Jose, CA 28.0% -23.3% 18.8% 24.5% Los Angeles, CA 19.4% -7.6% 4.4% 10.1% Chicago, IL 14.8% -32.4% 24.9% 10.5% San Diego, CA 11.9% 26.9% 5.5% 6.8% Phoenix, AZ 9.9% -8.5% 7.8% 16.7% Washington, DC 1.1% -16.1% 17.7% 14.9% Miami, FL 0.6% -18.9% 8.2% 5.9% Sacramento, CA -0.8% 17.6% 11.1% 8.3% Philadelphia, PA -1.9% -36.1% 3.4% 4.2% Cleveland, OH -3.7% -35.1% 28.9% 8.2% San Francisco, CA -4.2% -9.7% 18.9% 19.8% Tampa, FL -4.5% -25.6% 12.8% 5.2% Las Vegas, NV -6.8% -8.6% 148.2% 15.1% Jacksonville, FL -9.5% -40.2% 19.4% 1.4% Denver, CO -11.1% -22.1% 24.0% 21.9% Seattle, WA -11.8% -45.8% 22.7% -2.5% Minneapolis, MN -12.5% -26.7% 44.4% 26.4% Boston, MA -13.4% -12.7% 23.1% 17.0% New York, NY -16.4% -31.3% 29.7% 9.3% Detroit, MI -17.7% -20.6% -5.9% 7.0% Columbus, OH -20.0% -27.2% 19.2% 20.1% Atlanta, GA -21.8% -38.0% 9.2% 12.5% Milwaukee, WI -34.1% -35.3% 21.0% 25.9% Charlotte, NC -43.8% -32.2% 5.4% 4.1% St. Louis, MO % 26.8% 50.6% 27.1% Manhattan Condominium -61.2% -6.8% 1.2% 3.6% Source: 28-Day RPX analytics as of 5/21/ Transaction counts represent the transactions included in the calculation of the RPX Daily Prices and may not reflect transaction volume in the market. 2 Historical transactions used to calculate changes in St. Louis include late-arriving data not included in published series Exhibit 11: Transaction Counts: Motivated 3 vs. Other Sales May 09 % Motivated Sales May 08 % Motivated Sales May 09 vs. May 08 T.C. Change (Motivated) May 09 vs. May 08 T.C. Change (Other) May 09 vs. Apr 09 T.C. Change (Motivated) May 09 vs. Apr 09 T.C. Change (Other) Composite 26.8% 18.9% 38.2% -12.1% 2.8% 23.4% Los Angeles 32.8% 29.9% 30.6% 14.6% -9.1% 12.6% Miami 19.2% 10.2% 88.5% -9.5% -7.2% 12.6% New York 6.8% 3.1% 84.7% -19.6% 19.3% 30.5% Phoenix 43.1% 23.5% 101.1% -18.1% -2.9% 17.6% Source: 28-Day RPX analytics as of 5/21/ Radar Logic defines motivated sales as foreclosure auction sales and liquidity-driven sales by financial institutions and foreclosure service firms.

14 Exhibit 12: Tradable MSAs The following graphs contain MSA price data and transaction counts available from Radar Logic. Source: 28-Day RPX analytics as of 5/21/2009

15 Exhibit 13: RPX Forwards: Historical Fixings Price fixings are established each trading day by a dealer poll and represent the midmarket expectation for the reference value to be published on the contract expiration date. Reference values represent the simple average of the 28-day RPX Daily Prices from the last five publication dates of each quarter (which correspond to transaction dates 63 days earlier). For the following charts, the RPX prices are plotted on a publication date basis. The names of the series indicate the dates in 2009 those price fixings were published. Exhibit 4: Forward Contract Implied HPA (Cumulative) as of 7/20/ day RPX on 5/21/2009 Dec 09 Dec 10 Dec 11 Dec 12 Dec MSA Composite $ % -12.1% -14.3% -14.5% -14.6% Los Angeles, CA $ % -22.1% -20.1% N/A N/A Miami, FL $ % -26.7% -24.6% N/A N/A New York, NY $ % -21.7% -21.7% N/A N/A Phoenix, AZ $ % -16.2% -13.3% N/A N/A Source: Official 28-Day RPX fixings as of 7/20/2009

16 About Radar Logic Radar Logic Incorporated, a real estate data and analytics company, calculates and publishes the Radar Logic Daily Prices. The prices track housing values for major U.S. metropolitan areas and are the basis of the Residential Property Index (RPX ), a market that enables real estate to be traded as a liquid asset, via property derivatives marketed by major financial institutions. RPX allows real estate and financial professionals to manage opportunity and risk, invest in real estate values without owning physical assets and effectively analyze markets using a consistent metric: price per square foot. Data in the RPX Monthly Housing Market Report reflect the 28-day aggregated value of Radar Logic Daily Prices. The price per square foot metric used significantly reduces the influence of property sizes on overall housing price trends, which can skew results. The Daily Prices for each MSA are not adjusted for seasonal variations. In some cases, Daily Prices may vary based on reporting characteristics within individual MSAs. The RPX Monthly Housing Market Report provides insight and detailed analysis of Radar Logic s 25 MSAs and the Manhattan Condo market. This study is based on the premise that there is not a national housing market; rather, each MSA, while having some economic influences in common, is influenced primarily by local conditions. The June 2009 RPX Monthly Housing Market Report will be released on August 20, 2009, at 12:01 AM EDT. RPX Analytics & Research Radar Logic offers specialized analytic services which allow real estate and financial professionals to view current and historical price per square foot and transaction count trends for all markets and sub-markets we track. MSAs can be segmented by location (zip code and county), property type (single family, multi-family and condo), property size, date range, and sale price. The database is derived from our neutral, public source records. Our data provide a means for all entities associated with or affected by housing prices to maintain market data streams on a constant, neutral and daily updated basis. For additional insight on this report or for inquiries about research or analytic products, please contact: Radar Logic Incorporated 180 Varick Street, Suite 502 New York, NY info@radarlogic.com 2009 Radar Logic Incorporated. All Rights Reserved. Data presented AS IS. Radar Logic does not make, and hereby expressly disclaims, any representation or warranty of any manner in connection with the information including, without limitation, with respect to its accuracy, completeness or fitness for any purpose.