Chapter 25: Data Challenges in Measuring Real Estate Periodic Returns © 2014 OnCourse Learning. All Rights Reserved. 1 Three Major Types/Sources of Indices of Commercial Property Asset Prices & Investment Performance in the U.S… Index Type: Appraisal-based Source Examples • Based on professional appraisals of individual properties • Typically self-reported by ERISA investmt mgrs • NCREIF Property Index (NPI) • Investment Property Databank (IPD) Transactions-based • Based on actual prices of traded properties in private asset mkt • Need to control for “apples vs oranges” problem different assets traded each period, as by repeat-sales indices • Moody’s/RCA CPPI • RCA CPPI • CoStar Commercial Repeat-Sales Index (CCRSI) Stock Mkt-based • REIT share price data, delevered & filtered to represent “pure” property mkt segments • FTSE-NAREIT PureProperty® Index © 2014 OnCourse Learning. All Rights Reserved. 2 Section 25.1 “Macro-level Valuation”: Valuing aggregates of many properties at once (e.g., portfolios, indexes, entities like REITs or partnerships). Most basic macro-level valuation problem is valuing static portfolios… © 2014 OnCourse Learning. All Rights Reserved. 3 “Static Portfolio”: A portfolio consisting of a constant fixed set of properties (the same properties over time). “Static Portfolio Valuation”: The value of the portfolio is the sum of the values of all the individual properties in the portfolio (i.e., simple crosssectional aggregation of values, across the properties in the portfolio). Sounds simple: we know how to value individual properties (Chs.10-12), and we know how to add… But in fact some additional considerations become important at the macro-level. © 2014 OnCourse Learning. All Rights Reserved. 4 Most fundamentally, you must understand: The trade-off that exists between: VALUATION PRECISION (minimizing random error) VS. VALUATION “CURRENTNESS” (minimizing temporal lag bias) To begin, let’s go back to some basics at the micro-level of individual property valuation… © 2014 OnCourse Learning. All Rights Reserved. 5 Brief digression (fundamental background) back to… Chapter 12 Appendix: Noise & Values in Private R.E. Asset Mkts: Basic Valuation Theory… Understand the difference between: • Inherent Value • Investment Value • Market Value • Reservation Price • Transaction Price. © 2014 OnCourse Learning. All Rights Reserved. 6 Inherent Value: Maximum value a given user would be willing (and able) to pay for the subject property, if they had to pay that much for it (or, for a user who already owns the property, the minimum they would be willing to sell it for), in the absence of any consideration of the market value (“exchange value”) of the property. – Based on usage value of the property. Investment Value: Inherent value for a non-user owner (a “landlord”), i.e., for an investor. Market Value: Most likely or expected sale price of the subject property (mean of the ex ante transaction price probability distribution). Reservation Price: Price at which a market participant will stop searching and stop negotiating for a better deal and will close the transaction. Transaction Price: Actual price at which the property trades in a given transaction. Only the last of these is directly empirically observable. © 2014 OnCourse Learning. All Rights Reserved. 7 Consider a certain type of property… • There are many individual properties, examples of the type, • With many different owners. • Because the owners are heterogeneous, there will be a wide dispersion of “inherent values” that the owners place on the properties (e.g., like “investment value” ) because IV differs across investors. • We can represent this dispersion by a frequency distribution over the inherent values. . . Number of agents Ow ner Inherent Value Frequency Distributions (as of a single point in time) Owners Value ($/SF) Ow ners © 2014 OnCourse Learning. All Rights Reserved. 8 Consider a certain type of property… • There are also many non-owners of this type of property, • Potential investors. • Because these non-owners are also heterogeneous, there will be a wide dispersion of their IV values for this type of property as well. • Another frequency distribution over the inherent values . . . Number of agents Non-ow ner Inherent Value Frequency Distributions (as of a single point in time) Non-owners Value ($/SF) Non-ow ners © 2014 OnCourse Learning. All Rights Reserved. 9 Consider a certain type of property… • There will usually be overlap between the two distributions. . . Number of agents Ow ner & Non-ow ner Inherent Value Frequency Distributions (as of a single point in time) Non-owners Owners Value ($/SF) Ow ners Non-ow ners • It makes sense for the owners’ distribution to be centered to the right of the non-owners’ distribution, because of past selection: • Those who have placed higher values on the type of property in question are more likely to already own some of it. © 2014 OnCourse Learning. All Rights Reserved. 10 Because there is overlap, there is scope for trading of assets. (Recall from Ch.7 how investor heterogeneity underlies the investment industry.) Number of agents Ow ner & Non-ow ner Inherent Value Frequency Distributions (as of a single point in time) Non-owners Owners Value ($/SF) Ow ners Non-ow ners There is a mutual benefit from some non-owners whose IV values exceed those of some owners getting together and trading: • A price (P) can be found such that: IV(owner) < P < IV(non-owner). NPVIV(non-owner) = IV(non-owner) – P > 0 NPVIV(owner) = P - IV(owner) > 0 © 2014 OnCourse Learning. All Rights Reserved. 11 Because there is overlap, there is scope for trading of assets. Number of agents Ow ner & Non-ow ner Inherent Value Frequency Distributions (as of a single point in time) Non-owners Owners Value ($/SF) Ow ners Non-ow ners The number of non-owners willing to trade equals the area under the nonowner distribution to the right of the trading price. The number of owners willing to trade equals the area under the owner distribution to the left of the trading price. If permitted in the society, a real estate asset market will form and begin operation . . . © 2014 OnCourse Learning. All Rights Reserved. 12 Number of agents Ow ner & Non-ow ner Inherent Value Frequency Distributions (as of a single point in time) Non-owners Inherent values tend to be widely dispersed, reflecting investor heterogeneity. Owners Value ($/SF) Ow ners Non-ow ners Number of agents Buyers & Sellers Reservation Price Frequency Distributions (as of a single point in time) Buyers (Demand) Sellers (Supply) MV Value ($/SF) Sellers Buyers The operation of the asset mkt creates “price discovery” & “information aggregation”, which causes agents’ “reservation prices” (the price at which they will stop searching or negotiating and trade) to collapse around the midpoint of the overlap, the “mkt clearing price” (MV). (Less interested owners & non-owners effectively drop out of the distributions.) 13 Inherent Values Number of agents Ow ner & Non-ow ner Inherent Value Frequency Distributions (as of a single point in time) Non-owners Owners Value ($/SF) Ow ners Non-ow ners © 2014 OnCourse Learning. All Rights Reserved. 14 Reservation Prices Number of agents Buyers & Sellers Reservation Price Frequency Distributions (as of a single point in time) Buyers (Demand) Sellers (Supply) MV Value ($/SF) Sellers © 2014 OnCourse Learning. All Rights Reserved. Buyers 15 Reservation Prices Number of agents Buyers & Sellers Reservation Price Frequency Distributions (as of a single point in time) Buyers (Demand) Sellers (Supply) MV Value ($/SF) Sellers Buyers Reservation Prices are influenced not only by agents’ inherent values and perceptions of the market value, but also by agents’ search costs and degree of certainty about their value perceptions. © 2014 OnCourse Learning. All Rights Reserved. 16 Number of agents Buyers & Sellers Reservation Price Frequency Distributions (as of a single point in time) Buyers (Demand) Sellers (Supply) MV Value ($/SF) Sellers Buyers Market Value equals market clearing price, at which number of buyers (to right of price under buyer distribution) . . . © 2014 OnCourse Learning. All Rights Reserved. 17 Number of agents Buyers & Sellers Reservation Price Frequency Distributions (as of a single point in time) Buyers (Demand) Sellers (Supply) MV Value ($/SF) Sellers Buyers Market Value equals market clearing price, at which number of buyers (to right of price under buyer distribution) equals number of sellers (to left of price under seller distribution). © 2014 OnCourse Learning. All Rights Reserved. 18 The more “informationally efficient” is the asset market, the more effective is the price discovery and the information aggregation. The market learns from itself (about the value of the type of asset being traded in the market). In the extreme, the distributions on both sides of the market (the buyers and the sellers) will collapse onto the single, market-clearing price, at which the number of buyers equals the number of sellers: Number of agents Buyers & Sellers Reservation Price Frequency Distributions (as of a single point in time) Buyers (Demand) Sellers (Supply) P Value ($/SF) Sellers Buyers Hence, observed prices exactly equal market values. This is approximately what happens in the stock market. © 2014 OnCourse Learning. All Rights Reserved. 19 Real estate markets are not that informationally efficient. There is price dispersion. Probability Possible Transaction Price Probability Distribution The mean of this distribution (“expected price”) is the market value (MV) MV Prices Observed transaction prices are distributed around the market value. © 2014 OnCourse Learning. All Rights Reserved. 20 It is impossible to know exactly what is the market value of any property at any point in time. Observed prices are “noisy” indications of value. MV can be estimated by observing the distribution of transaction prices, using statistical or appraisal techniques. Possible Transaction Price Probability Distribution MV can be estimated more accurately: Probability • The larger the number of transactions (more frequent trading, “denser market”), & MV Prices • The more homogeneous the assets traded in the mkt. • Nevertheless . . . All estimates of MV (whether appraisal or statistical) contain “error”. © 2014 OnCourse Learning. All Rights Reserved. 21 Technical Issues in R.E. Indexes Index return in period t : Vt Vt 1 rt Vt 1 Two Issues: • “Noise” Index value level Vt randomly dispersed around true population value (Pt): Vt = Pt ± ~t • “Lag”, Index value level Vt tends to be a blend of current and recent past population values, e.g.: Vt = (1/2)Pt + (1/2)Pt-L. 25.1.3 The NOISE vs. LAG TRADE-OFF Example: You own a property. Would you rather have an estimate of value that is accurate to within 10% with no lag bias, or to within 2% but whose most likely value is what the property was worth 6 months ago?… Your answer probably depends on how you are going to use the appraisal: • Are you just interested in the value of that one property? • Or will you be combining that property’s valuation with many others to arrive at the value of an entire portfolio or index? In the latter case, the purely random error in the property valuation estimate will tend to cancel out with other errors and diversify away, but the temporal lag bias will not go away. © 2014 OnCourse Learning. All Rights Reserved. 23 25-3 Noise-vs-Lag Trade-off Frontiers with Disaggregate and Aggregate Valuation Methodologies Lag Bias 0 mos TAgg “Corner Solution” Optimum @ Zero Lag. C UAgg TDis TAgg 6 mos A B TDis 15% 8% UDis 2% 0% Random Error (σε ) Reduced Random Error © 2014 OnCourse Learning. All Rights Reserved. 24 The Noise vs Lag Trade-off (Sect. 25.1.3) . . . •To reduce random estimation error, more empirical value observations (transactions data) are required. • To obtain more empirical value obs, transactions must be taken from a longer span of history (reaching further back in time). • The “Square Root of N Rule” provides diminishing returns in this process (a concave trade-off frontier). Reduced random noise © 2014 OnCourse Learning. All Rights Reserved. 25 The Noise vs Lag Trade-off . . . • This is the trade-off at the disaggregate (individual property) level. • This is the level that is relevant for appraisal valuations. Reduced random noise © 2014 OnCourse Learning. All Rights Reserved. 26 The Noise vs Lag Trade-off . . . • Property value estimates are made for users of this information. • These users dislike both random estimation error and temporal lag bias, but logically: • with diminishing marginal utility for both types of accuracy (see U0 indiff.curve). • Point A is a typical optimal individual property value estimation (an appraisal maximizing the client’s utility of the appraisal). A • It has a certain amount of random error and a certain amount of temporal lag bias. U0 Reduced random noise © 2014 OnCourse Learning. All Rights Reserved. 27 25.1.4: Noise-vs-Lag Trade-off Frontiers with Disaggregate (Traditional Appraisal) and Aggregate (Transactions Based Regression) Valuation Methodologies NA = Disaggregate (Appraisal) Optimal Sample Size (# comps) = nA(LA+1), nA = comps density/period; NC = Aggregate (Transactions Based Regression) Optimal Sample Size (obs per period); Q = Number of Market Segments in the Index Population (as multiple of number used by appraiser). Lag Bias in Periods Exh. 25-3 “Corner Solution” Optimum @ Zero Lag. Transactions Based Index C 0 TAgg TDis LA /2 A B U1 TAgg TDis U0 1/NA 1/NC 1/(QNA) 0 Random Error (fraction of σε2 ) Reduced random error © 2014 OnCourse Learning. All Rights Reserved. 28 Noise-vs-Lag Trade-off Frontiers with Disaggregate (Traditional Appraisal) and Aggregate (Transactions Based Regression) Valuation Methodologies NA = Disaggregate (Appraisal) Optimal Sample Size (# comps) = nA(LA+1), nA = comps density/period; NC = Aggregate (Transactions Based Regression) Optimal Sample Size (obs per period); Q = Number of Market Segments in the Index Population (as multiple of number used by appraiser). Lag Bias in Periods Exh. 25-3 “Corner Solution” Optimum @ Zero Lag. Transactions Based Index C 0 C: Index is optimized at aggregate level. TAgg TDis B: Index is aggregation of optimal individual appraisals. LA A B U1 TAgg TDis U0 1/NA 1/NC 1/(QNA) 0 Random Error (fraction of σε2 ) Reduced random error © 2014 OnCourse Learning. All Rights Reserved. 29 Section 25.2 II. Problems in real estate periodic returns data…(25.2) Background: From values to returns… Recall the definition of the periodic return: Vt Vt 1 CFt Vt Vt 1 CFt rt g t yt Vt 1 Vt 1 Vt 1 We need: Vt = True value of asset as of the end of period “t” in time. Vt-1 = True value of asset as of the end of period “t-1” in time. OK for publicly-traded securities (at quarterly frequency). But for private real estate, “we have a problem”… © 2014 OnCourse Learning. All Rights Reserved. 30 In fact, we have two problems: Observed value of Vt is measured with random error, exhibits “noise”. Observed value of Vt exhibits “temporal lag bias”, as if computed from a trailing moving average across time. © 2014 OnCourse Learning. All Rights Reserved. 31 Here is a picture of the typical pure effect of noise (alone) on an index of cumulative asset or portfolio value levels: Exh.25-4B 1.4 1.3 1.2 1.1 1.0 True Noisy 0.9 0.8 0.7 0.6 0.5 1 11 21 31 41 TIME How does this “sawtooth” effect result from random noise? . . . © 2014 OnCourse Learning. All Rights Reserved. 32 Aside: How does this “sawtooth” effect result from random noise? . . . Value Suppose this is the true (unobservable) history of real estate values over time: And suppose valuation error equals +10% or -10%, randomly over time (independent errors), as if from the flips of a coin… © 2014 OnCourse Learning. All Rights Reserved. Time 33 Random valuation error adds excess apparent volatility, that is transient (“mean-reverts”) over time: Exh.25-4B 1.4 1.3 1.2 1.1 1.0 True Noisy 0.9 0.8 0.7 0.6 0.5 1 11 21 31 41 TIME © 2014 OnCourse Learning. All Rights Reserved. 34 Here is a picture of the typical pure effect of temporal lag bias (alone) on an index of cumulative asset or portfolio value levels: Exh.25-5B 1.4 1.3 1.2 1.1 1.0 True Lagged 0.9 0.8 0.7 0.6 0.5 1 11 21 31 41 TIME How does this lag effect result from historical temporal aggregation? © 2014 OnCourse Learning. All Rights Reserved. 35 Aside: How does the lag effect result from historical temporal aggregation? . . . Value Suppose this is the true (unobservable) history of real estate values over time: Time And suppose appraisers use two comps which they weight equally to estimate the current period’s value, one comp is current, the other from the previous period (& ignore random error to focus on the pure temporal aggregation effect). © 2014 OnCourse Learning. All Rights Reserved. 36 Temporal aggregation results in an apparent index that is both lagged and smoothed (less volatile) compared to the true values: Exh.25-5B 1.4 1.3 1.2 1.1 1.0 True Lagged 0.9 0.8 0.7 0.6 0.5 1 11 21 31 41 TIME © 2014 OnCourse Learning. All Rights Reserved. 37 Here is a picture of the typical appraisal-based index, which includes both random noise & temporal lag: Exh.25-6B 1.4 1.3 1.2 1.1 1.0 True Appraised 0.9 0.8 0.7 0.6 0.5 1 11 21 31 41 TIME How much of each type of error depends on how many properties (appraisals) are included in the portfolio or index, and on how much lagging the appraisers had to do at the individual property (disaggregate) valuation level. © 2014 OnCourse Learning. All Rights Reserved. 38 The two pure effects and appraisals . . . Exh.25-6B 1.4 1.3 1.2 1.1 True 1.0 Noisy Lagged 0.9 Appraised 0.8 0.7 0.6 0.5 1 11 21 31 41 TIME Of course, the “true” value index would be unobservable in the real world. © 2014 OnCourse Learning. All Rights Reserved. 39 These types of valuation errors can cause a number of problems: · “Apples vs oranges” comparison betw R.E. and securities returns · Misleading estimates of R.E. ex post investment performance · Misleading estimates of R.E. risk and co-movement: · (e.g., R.E. covariance or is underestimated.) · Out-of-date information about property mkts: · (e.g, have mkts “peaked”, or are they still “rising”?) How to understand, recognise, and deal with the returns data problem (Ch.25)… © 2014 OnCourse Learning. All Rights Reserved. 40 Section 25.3 25.3. What is Truth? Lags and the Timeline of Price Discovery in Real Estate Suppose publicly-observable “news” arrives at a point “t” in time. This news is relevant to the value of real estate assets. What will happen? 1st) REIT share prices quickly and fully respond to the news, changing to the newly appropriate level almost immediately (probably within a day or two). We can represent this as: V*REITt = VREITt V*REITt = Observed REIT value, as of end of period “t”. VREITt = True REIT value, as of end of period “t”. (Maybe a little “overreaction”, then correction?…) (Maybe some “spurious” movements: things REIT investors care about that property investors don’t care about?…) (But at least they move quickly and in the right direction in response to relevant news.) © 2014 OnCourse Learning. All Rights Reserved. 41 2nd) Property market liquid asset values respond more gradually to the news: Vt = 0 VREITt + 1 VREITt-1 + . . . , where 0<t<1, and t=1 Vt = Property market value (“liquid” value, “bid price”) as of end of “t”. VREITt = Full-information value (as if it were a REIT) in that same market. Note: Vt VTt . . . Transaction prices (VTt)observed in the property asset market at time “t” are not generally the same as fully liquid market prices (especially in a down-market). This is because liquidity is in fact not constant across time in property markets, as many property owners do not require constant liquidity in their real estate holdings, so they tend to hold properties off the market during “down markets” and to sell more properties during “up markets”. © 2014 OnCourse Learning. All Rights Reserved. 42 Here is what pro-cyclical variable liquidity looks like in the NCREIF Index: NCREIF Prices & Turnover Ratios, 1984-2001 0.2 20% 15% 0 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 -0.1 10% -0.2 -0.3 5% Percent Properties Sold Log Price Level 0.1 -0.4 -0.5 0% Year © 2014 OnCourse Learning. All Rights Reserved. 43 3rd) Empirically observable transaction prices in the property market will even more gradually reflect the news (at least during down-markets, when prices are falling): Vt 0Vt 1Vt 1 2Vt 2 , where: period t. 1 Vt = Cross-sectnl avg transaction price in period t; Vt = Cross-sectnl avg liquid value (bid price) in © 2014 OnCourse Learning. All Rights Reserved. 44 4th) Appraised values of properties will respond even more gradually (Appraisers tending to be more “backward-looking”, dependent on transaction price observations, than property market participants who make or lose money depending on how well they can be “forward-looking”.) We can represent this as: V *t Vt (1 )V *t 1 0<a<1, a = "Confidence Factor", (1-)=Smoothing factor. where V*t is property appraised value as of the end of quarter "t" and Vt is the average empirically observable transaction price during quarter “t”. © 2014 OnCourse Learning. All Rights Reserved. 45 5th) Indexes of appraisal-based returns may respond even more slowly to the news, if all properties in the index are not reappraised every period, yet they are included in the index at their last appraised valuation. Problem of “stale” valuations in the index, e.g.: V**t = (¼)V*t + (¼)V*t-1 + (¼)V*t-2 + (¼)V*t-3 where V**t is the index value in quarter t. If more of the properties are reappraised in the fourth calendar quarter (as with the NCREIF Index), then something like the following model might well represent the index in the 4th quarter of every year: V**t = (1/2)V*t + (1/6)V*t-1 + (1/6)V*t-2 + (1/6)V*t-3 This will make the index more “up-to-date” at the end of the 4th quarters than it is in the other quarters, and it will impart “seasonality” into the quarterly index returns. © 2014 OnCourse Learning. All Rights Reserved. 46 25.3.1 Here is a schematic picture of how this time-line of price discovery might play out in response to the arrival of a single piece of (bad) news… Exhibit 25-7 100 1 2 3 4 5 90 1 = REIT Index 2 = Liquid Property Value 3 = Empirical Transactions 4 = Appraised Values 5 = Staggered Appraisal Index (NCREIF) t © 2014 OnCourse Learning. All Rights Reserved. Time 47 Summary of lagged incorporation of “news” into values: Time t News arrives. REIT Prices respond Property Mkt Val Fully reflects Appraised Values Fully reflect © 2014 OnCourse Learning. All Rights Reserved. Index Fully reflects 48 What this looks like in the real world: The time line of real estate price discovery… (1)Public (2)Const.Liq (3)Var.Liq. (5)Appraisal: Four Definitions and Measures of U.S. Commercial Property Values, 2000-2011. (1) REIT-based: FTSE-NAREIT PureProperty (daily) (2) Constant-Liquidity: NCREIF NTBI Demand (3) Trasaction Prices: NCREIF NTBI (*NTBI Demand index starting value set to equalize average historical price and demand 9/23/2011 1/14/2011 5/7/2010 8/28/2009 12/19/2008 4/11/2008 8/3/2007 11/24/2006 3/17/2006 7/8/2005 10/29/2004 2/20/2004 6/13/2003 10/4/2002 1/25/2002 5/18/2001 (5) Stale-appraisal-based: NPI 9/8/2000 1950 1900 1850 1800 1750 1700 1650 1600 1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 1050 1000 950 900 12/31/1999 1999 Value = 1000* Exh. 25-8 indices value levels. Horizontal-axis is all weekdays; gaps in data are stock market holidays. ) © 2014 OnCourse Learning. All Rights Reserved. 49 25.4.1: Overview of Different Types of Indices: Different types of indices have different strengths & weaknesses Index Type: Appraisal-based Strengths Weaknesses • Can be available when others not • Strong profession & tradition in some countries • Opinions not actual prices • Tend to lag & smooth market values • Can be subject to influence Transactions-based • Actual prices directly reflect mkt equilibrium • Objective info, less susceptible to manipulation • Requires large historical database • Statistical models Stock Mkt-based • REITs (or property “pure plays”) traded in many countries • Uses information efficiency & liquidity of stock mkt • Leading indicator, daily updates • Some countries have few REITs, or short history, or thin market • Information only indirect about actual property mkt Therefore… © 2014 OnCourse Learning. All Rights Reserved. 50 First: NCREIF Property Index (NPI), since 1982, appraisal-based (X-axis is all weekdays; gaps in data are stock market holidays.) Composites are value-weighted. 9/23/2011 1/14/2011 5/7/2010 8/28/2009 12/19/2008 4/11/2008 8/3/2007 11/24/2006 3/17/2006 7/8/2005 10/29/2004 2/20/2004 6/13/2003 10/4/2002 1/25/2002 5/18/2001 NCREIF NPI (appraisal-based) 9/8/2000 2100 2050 2000 1950 1900 1850 1800 1750 1700 1650 1600 1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 1050 1000 950 900 12/31/1999 1999 Value = 1000 U.S. Institutional Commercial Property Composite Capital Value, 2000-2011: Various types of indices... Quarterly Based on small (but important) population (pension funds), law requires regular re-appraisal. (U.S. GAAP does not require appraisals.) Tracks same-property valuations & total returns. Appraisals lag & smooth market values, but widely accepted & used by industry. 2005: NCREIF-based “TBI”, transaction based NCREIF NPI (appraisal-based) (X-axis is all weekdays; gaps in data are stock market holidays.) Composites are value-weighted. 9/23/2011 1/14/2011 5/7/2010 8/28/2009 12/19/2008 4/11/2008 8/3/2007 11/24/2006 3/17/2006 7/8/2005 10/29/2004 2/20/2004 6/13/2003 10/4/2002 1/25/2002 5/18/2001 NCREIF TBI (transaction based) 9/8/2000 2100 2050 2000 1950 1900 1850 1800 1750 1700 1650 1600 1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 1050 1000 950 900 12/31/1999 1999 Value = 1000 U.S. Institutional Commercial Property Composite Capital Value, 2000-2011: Various types of indices... Quarterly Originally developed at MIT Center for Real Estate, now produced by NCREIF, based on same population as NPI, but transaction prices of sold properties. “Hedonic price model” approximated by ratio: TBI(t) = (P(t)/A(t))NPI(t). 2006: RCA-based “CPPI”, repeat-sales index (transactions based) NCREIF NPI (appraisal-based) NCREIF TBI (transaction based) (X-axis is all weekdays; gaps in data are stock market holidays.) Composites are value-weighted. 9/23/2011 1/14/2011 5/7/2010 8/28/2009 4/11/2008 8/3/2007 12/19/2008 Quarterly 11/24/2006 3/17/2006 7/8/2005 10/29/2004 2/20/2004 6/13/2003 10/4/2002 1/25/2002 5/18/2001 Moodys/RCA CPPI 9/8/2000 2100 2050 2000 1950 1900 1850 1800 1750 1700 1650 1600 1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 1050 1000 950 900 12/31/1999 1999 Value = 1000 U.S. Institutional Commercial Property Composite Capital Value, 2000-2011: Various types of indices... 1st CRE index using repeat-sales methodology. Based on larger population of properties than NCREIF: Real Capital Analytics (RCA) database of all sales > $2,500,000. Tracks same-property prices (not total returns). Similar method to Case-Shiller. 2012: FTSE-NAREIT PureProperty™ Indices, stock mkt based… NCREIF NPI (appraisal-based) NCREIF TBI (transaction based) Moodys/RCA CPPI (X-axis is all weekdays; gaps in data are stock market holidays.) Composites are value-weighted. 9/23/2011 1/14/2011 5/7/2010 8/28/2009 12/19/2008 4/11/2008 8/3/2007 11/24/2006 3/17/2006 7/8/2005 10/29/2004 2/20/2004 6/13/2003 10/4/2002 1/25/2002 5/18/2001 FTSE/NAREIT PureProperty (REIT-based) 9/8/2000 2100 2050 2000 1950 1900 1850 1800 1750 1700 1650 1600 1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 1050 1000 950 900 12/31/1999 1999 Value = 1000 U.S. Institutional Commercial Property Composite Capital Value, 2000-2011: Various types of indices... PureProperty Daily, Others Quarterly Daily-updated index, based on de-geared REIT share prices. REITs are “pure plays” in commercial property. Index statistically infers movements in underlying property assets implied by movements in REIT share prices. Uses information efficiency and liquidity of stock market. How repeat-sales price index works… 25.4.2: Repeat-Sales Indices. Numerical Example of Repeat-Sales Regression Model True Price Index True Capital Return Property # 1 Property # 2 Property # 3 2006 1.00 $100,000 $200,000 No Data Prices Observed at Ends of Years: 2007 2008 1.00 1.10 0% 10% No Data No Data No Data $220,000 $300,000 No Data 2009 1.045 -5% $104,500 No Data $313,500 1.10 Prop # 2 1.045 Prop # 1 Prop # 3 1.00 1.00 2007 2008 (See posted repeat-sales index simulator Excel file.) 2009 55 How repeat-sales price index works… Numerical Example of Repeat-Sales Regression Model Regression model: Y = a2007(X2007) + a2008(X2008) + a2009(X2009) Y = Log price difference (LN of ratio 2nd sale price / 1st sale price). Xyr = Dummy variable (= 1 if yr betw 1st & 2nd sales; yr during investr holding). ayr = Parameters to be estimated = True log price ratio during yr. RSR Estimation Data Observation # 1 Observation # 2 Observation # 3 Y value = LN(Ps/Pf) LN(1.045) LN(1.10) LN(1.045) X2007 value 1 1 0 X2008 value 1 1 1 X2009 value 1 0 1 Estimation: Solve simultaneous equations (1 eq per obs): LN(1.045) = a2007(1) + a2008(1) + a2009(1) LN(1.045) = a2007 + a2008 + a2009 Eq.(1) LN(1.100) = a2007(1) + a2008(1) + a2009(0) LN(1.100) = a2007 + a2008 Eq.(2) LN(1.045) = a2007(0) + a2008(1) + a2009(1) LN(1.045) = + a2008 + a2009 Eq.(3) Eq.(2) a2008 = LN(1.1) – a2007 Eq.(2&3) a2009 = LN(1.045) – LN(1.1) + a2007 Sub into Eq.(1) LN(1.045) = a2007 + [LN(1.1) – a2007] + [LN(1.045) – LN(1.1) + a2007] a2007 = 0 = LN(1/1). Sub back into (2)&(3) a2008 = LN(1.1) = LN(1.1/1), a2009 = LN(1.045/1.1). Exponentiate to retrieve true index price ratios: 1.00, 1.10, 1.045/1.1. Retrieved returns: 0% (2007), 10% (2008), -5% (2009). 56 How repeat-sales price index works… Numerical Example of Repeat-Sales Regression Model Thus, model retrieves true returns (including 2009 negative): 2007 0% 2008 10% 2009 -5% Even though no single repeat-sale observation reveals any one return, and none of the investments had a price decline… 1.10 Prop # 2 1.045 Prop # 1 Prop # 3 1.00 1.00 2007 2008 © 2014 OnCourse Learning. All Rights Reserved. 2009 57 How repeat-sales price index works… Numerical Example of Repeat-Sales Regression Model Thus, model retrieves true returns (including 2009 negative): 2007 0% 2008 10% 2009 -5% Even though no single repeat-sale observation reveals any one return, and none of the investments had a price decline… This is a general result: As long as data contains at least one sale (obs) per period, model can find return in each period. In real world, there will also be random dispersion of individual transaction prices around market price. But there will also be more than one observation per period. Statistical techniques can optimize the resulting estimates (e.g., OLS, WLS, ridge, time-wtd dummies). © 2014 OnCourse Learning. All Rights Reserved. 58 See repeat-sales index simulator Excel file on book’s CD… 1.30 True index Estd index 1.20 Obs 1 (ratio) Obs 2 (ratio) 1.10 Obs 3 (ratio) Obs 4 (ratio) 1.00 Obs 5 (ratio) Obs 6 (ratio) Obs 7 (ratio) 0.90 Obs 8 (ratio) Obs 9 (ratio) Obs 10 (ratio) Obs 11 (ratio) 0.80 Obs 12 (ratio) 0.70 2006 2007 2008 2009 © 2014 OnCourse Learning. All Rights Reserved. 59 “Institutional” (aka “Investment Grade”) properties (larger, in primary mkts) exhibit different price behavior than smaller (“mom & pop”) properties, as seen in CCRSI… CoStar CCRSI, Investment vs General Commercial Properties: Exh. 25-9 Same-property (repeat-sales) Prices, 2000-2012 220 CCRSI General Property 210 CCRSI Investment Property 200 190 1999 Value = 100 180 170 160 150 140 130 2001-10: General Investment All 120 110 RS obs 70% 30% 100% RS $vol 21% 79% 100% 12/1/2011 6/1/2011 12/1/2010 6/1/2010 12/1/2009 6/1/2009 12/1/2008 6/1/2008 12/1/2007 6/1/2007 12/1/2006 6/1/2006 12/1/2005 6/1/2005 12/1/2004 6/1/2004 12/1/2003 6/1/2003 12/1/2002 6/1/2002 12/1/2001 6/1/2001 12/1/2000 6/1/2000 12/1/1999 100 Data source: CoStar Group Inc. Index values as of June 2012. Reflects different sources of financing (non-bank vs bank), different owner/investor clienteles (natl/intl instns vs local/users), different asset mkt segments. Even within the “institutional” properties, there is some asset mkt segmentation… Exh. 25-10 220 200 “Major” = Bos, NYC, DC, Chi, SF, LA. “Non-Major” = All else. 180 2000 = 100 Moody's/RCA CPPI, Major & Non-Major Markets Composite Indices: Same-property Price Evolution (Repeat-Sales) 160 140 120 100 6.2012 12.2011 6.2011 12.2010 6.2010 12.2009 6.2009 12.2008 6.2008 12.2007 6.2007 12.2006 6.2006 Major 12.2005 6.2005 12.2004 6.2004 12.2003 6.2003 12.2002 6.2002 12.2001 6.2001 12.2000 80 Non-Major Source: Real Capital Analytics. Index histories as of July 2012. This is all larger properties ($2.5M+), corresponds roughly to CoStar “Investment” properties. 61 Composite Indices: Value-Weighted Moody’s/RCA Repeat-Sales Index Scheme, Based on 10 “Buildingblock” segment-specific indices… All Apt Apt Composite Indices: Value-Weighted Apt Maj Apt Not Ind Maj Com Ind Ind Not Major Mkts OffC Maj OffCBD OffC Not OffS Maj OffSub OffS Not Ret Ret Maj Ret Not NotMaj Mkts Two Supplemental Composites Not Integrated Into the Overall Composite Scheme © 2014 OnCourse Learning. All Rights Reserved. 62 12.2000 3.2001 6.2001 9.2001 12.2001 3.2002 6.2002 9.2002 12.2002 3.2003 6.2003 9.2003 12.2003 3.2004 6.2004 9.2004 12.2004 3.2005 6.2005 9.2005 12.2005 3.2006 6.2006 9.2006 12.2006 3.2007 6.2007 9.2007 12.2007 3.2008 6.2008 9.2008 12.2008 3.2009 6.2009 9.2009 12.2009 3.2010 6.2010 9.2010 12.2010 3.2011 6.2011 9.2011 12.2011 3.2012 Exh. 25-11A Major Mkts Apt CPPI Major Markets Building-Block Indices 240 220 200 180 160 140 120 100 80 60 Major Mkts Ind Major Mkts CBD Off Major Mkts Sub Off © 2014 OnCourse Learning. All Rights Reserved. Major Mkts Ret 63 12.2000 3.2001 6.2001 9.2001 12.2001 3.2002 6.2002 9.2002 12.2002 3.2003 6.2003 9.2003 12.2003 3.2004 6.2004 9.2004 12.2004 3.2005 6.2005 9.2005 12.2005 3.2006 6.2006 9.2006 12.2006 3.2007 6.2007 9.2007 12.2007 3.2008 6.2008 9.2008 12.2008 3.2009 6.2009 9.2009 12.2009 3.2010 6.2010 9.2010 12.2010 3.2011 6.2011 9.2011 12.2011 3.2012 Exh. 25-11B Non-Major Mkts Apt CPPI Non-Major Markets Building-Block Indices 240 220 200 180 160 140 120 100 80 60 Non-Major Mkts Ind Non-Major Mkts CBD Off Non-Major Mkts Sub Off © 2014 OnCourse Learning. All Rights Reserved. Non-Major Mkts Ret 64 6.2001 9.2001 12.2001 3.2002 6.2002 9.2002 12.2002 3.2003 6.2003 9.2003 12.2003 3.2004 6.2004 9.2004 12.2004 3.2005 6.2005 9.2005 12.2005 3.2006 6.2006 9.2006 12.2006 3.2007 6.2007 9.2007 12.2007 3.2008 6.2008 9.2008 12.2008 3.2009 6.2009 9.2009 12.2009 3.2010 6.2010 9.2010 12.2010 3.2011 6.2011 9.2011 12.2011 3.2012 6.2012 9.2012 Market segmentation Need for index granularity… 240 Example of Transaction Price Index Granularity: Moodys/RCA CPPI, 6 Major Metros 220 200 180 160 140 120 100 80 60 Boston Chicago DC Metro LA Metro NYC Metro SF Metro 220 6.2001 9.2001 12.2001 3.2002 6.2002 9.2002 12.2002 3.2003 6.2003 9.2003 12.2003 3.2004 6.2004 9.2004 12.2004 3.2005 6.2005 9.2005 12.2005 3.2006 6.2006 9.2006 12.2006 3.2007 6.2007 9.2007 12.2007 3.2008 6.2008 9.2008 12.2008 3.2009 6.2009 9.2009 12.2009 3.2010 6.2010 9.2010 12.2010 3.2011 6.2011 9.2011 12.2011 3.2012 6.2012 9.2012 Market segmentation Need for index granularity… 240 Example of Transaction Price Index Granularity: Moodys/RCA CPPI, 9 Non-major Metros 200 180 160 140 120 100 80 60 Atlanta Denver Las Vegas Philadelphia/Baltimore Phoenix San Diego Seattle Miami/SoFla Austin/Dallas/Houston U.S. Real Home Prices & Business Cycles, 1890-2011 Robert Shiller Real Home Price Index, NBER Recessions 1890=100 shaded bars = GDP recessions… 200 190 180 US Real House Prices 1890 = 100 170 160 150 140 130 120 110 100 90 80 70 60 2010 2007 2005 2002 2000 1997 1995 1992 1990 1987 1985 1982 1980 1977 1975 1972 1970 1967 1965 1962 1960 1957 1955 1950 1940 1930 1920 1910 1900 1890 50 Real home prices somewhat cyclical & somewhat related to recessions, but not always. 2003-09 bubble & bust stands out: housingrecessionhousing. Note: Horizontal scale compressed prior to 1952. 67 Real GDP & Nominal Real Estate Prices: 1969-2011… CRE Same-Property Transaction Prices, Shiller Nominal Home Price Index, GDP 1969=100 shaded bars = GDP recessions… 1000 900 800 700 600 500 400 300 200 100 0 2011 2009 2007 2005 2003 2001 1999 1997 CRE Same-Property Prices 1995 1993 1991 1989 1987 1985 1983 1981 1979 1977 1975 1973 1971 1969 Real GDP Nominal Home Prices Home price LT trend > CRE price LT trend. CRE more cyclical. Home prices turned down before CRE prices in onset of GFC (2006-07), slower recovery afterwards. CRE prices turned down before home prices in onset of 1990-91 recession which had very little nationwide nominal home price fall. 68 Real GDP & Nominal CRE Prices: 1969-2011… Nominal CRE Same-Property Transaction Prices, Real GDP 1969=100 shaded bars = GDP recessions… 500 450 CRE Pk-Trgh Price: 400 350 “CRE Recession?” Real Nominal 1970s -39.3% -18.0% 1980-90s -43.1% -27.3% 2007-09 -35.9% -33.6% 300 250 200 150 CRE Boom: 100 50 Real Nominal 1970s-80s 23.3% 144.3% 1990s-2000s 88.5% 176.8% 0 2011 2009 2007 2005 2003 2001 1999 1997 1995 1993 1991 1989 1987 1985 1983 1981 1979 1977 1975 1973 1971 1969 Real GDP CRE Same-Property Prices 42 years, six recession episodes (7-yr cycle), three CRE cycles (14-yr cycle). Three recessions largely did not impact CRE price growth (1969-70, 1980-82, 2001). Three recessions associated with CRE price drops (1973-75, 1990-91, 2007-09). CRE price drop substantially preceded 73-75 & 90-91 recessions, barely preceded 07-09 recession. CRE most associated with 90-91 recession. Housing with 07-09. 69 Other Types of Real Estate Indices • Green Street Advisors CPPI: • Ad hoc methodology (GS estimates) • Estimates price REIT properties “would sell at” • Meant to be “leading indicator” • Based on transactions, brokers, REIT execs, etc • FTSE/NAREIT “PureProperty™” Indices: • Based on REIT share prices unlevered • Econometric inference of property valuations implied by stock market valuations of REITs • Based on tradable long/short portfolios of REITs • Uses information efficiency of stock mkt • Objective, replicable, leading indicator. © 2014 OnCourse Learning. All Rights Reserved. 70