World Economics - Insight , Analysis and Data

World Economics - Insight , Analysis and Data

House Price Indices: Does Measurement Matter?

Mick Silver - September 2011

A key factor in understanding the global recession is movements in residential property price indexes (RPPIs). However, more than one national RPPI is often compiled and disseminated for a country, each differing in methodology and results. The main methodological issues are considered with three case studies: the United Kingdom, the United States and the Russian Federation.

The significance of a housing bust 
The October 2009 Report to the G-20 Finance Ministers and Central Bank Governors on the Financial Crisis and Information Gaps[2] argues that data on dwellings and their associated price changes are critical ingredients for financial stability policy analysis. The six major banking crises in advanced countries since the mid–1970s were all associated with a housing bust (Reinhart and Rogoff, 2009). Claessens, Kose, and Terrones (2008, page 25) also find that “..recessions associated with house price busts are on average over a quarter longer than those without busts. Moreover, output declines (and corresponding cumulative losses) are typically much larger in recessions with busts, 2.2 (3.7) percent versus 1.5 (2.3) percent in those without busts. These sizeable differences also extend to the other macroeconomic variables, including consumption, investment and the unemployment rate.”[3] 

However, an understanding of deviations from equilibrium prices in housing markets requires reliable and, for international comparisons, consistently-measured, residential property price indices (RPPIs)—hereafter the terms RPPIs and house prices indexes (HPIs) are used interchangeably. RPPIs may also be used in the measurement of the cost of owner-occupied housing in consumer price indexes and inconsistencies in RPPI measurement practices may compromise the integrity and comparability of such measures (Diewert, 2004). RPPIs are particularly prone to methodological differences, which can undermine both within-country and cross-country analysis. It is a difficult but important area. There is an important empirical question as to whether measurement differences matter. 

Against this background, three case studies are considered in this paper to illustrate the problem—RPPIs for the United Kingdom, the United States, and the Russian Federation.


The potential for mismeasurement
House price indices depend for their data on the way in which houses are bought and sold and the type of records kept. Different agencies have different records and can and do compile their own measures. They may be based on the asking prices by realtors, the appraisal prices of mortgagees/tax authorities, or registered transaction prices. A house sold in one month will not have a corresponding sales price in each subsequent month, so there is a need to control for changes in the quality-mix of houses sold: higher proportions of more-expensive houses sold in a month should not manifest themselves as price increases.  Methods of adjusting for quality mix include the use of hedonic regressions, repeat sales data only, and mix-adjustments by weighting detailed strata. There is a need to weight price changes over different regions and over types of “houses” (apartments, detached, townhouses etc.) and the weights may be the relative number or the relative value of either transactions or the stock of houses. And house price index measurement can differ in many other respects including their coverage and the manner in which the quality-mix adjustment and weighted aggregation is implemented—Fenwick (2006), Hill (2011) and Eurostat (2011).

Extreme care is required when comparing the house price indices of different countries for which the nature of source data and methods employed may be quite different. But what of measuring house prices changes for an individual country? Surely that should be straightforward. Economists should have a single reliable measure upon which to base their work. The proliferation of house price indices in the UK illustrate that this is not the case.


United Kingdom House Price Indices
There are eight major residential property price indices in the UK based on different types of source data. Details of these methods can be found in review papers including Wood (2005), Dey-Chowdhury (2007) and the UK Government Statistical Service (2010)—though see also Carless (2011). The Land Registry records form the basis of both the Land Registry index, compiled by Calnea Analytics Limited, and the LSL/Acadametrics (Financial Times) index (AcadHPI). Prices are the registered transaction price on completion of the sale. The Halifax and Nationwide indices are based on their own mortgage approval records and the Department of Communities and Local Government (DCLG) index on all transactions bought with a mortgage issued by one of about 50 lenders.  Rightmove’s index is based on the asking prices of property included on Rightmove.co.uk. There are two survey-based indices, one carried out by the Royal Institution of Chartered Surveyors (RICS) based on the opinions of a sample of their members and the Hometrack survey which is based on the opinions of a sample of estate agents (realtors) and surveyors. These opinion-based “net balance” indicators are excluded from the analysis below as they are not designed for the analysis of change.

Do UK property price indices differ?
Figure 1 shows that in spite of the substantial methodological and data differences outlined below, there is a striking similarity in trend and timing of the turning points for annual changes (quarter-on-corresponding previous year’s quarter). Differences do exist, especially in the amplitude of the 2003/4 turning points and 2008/09 trough. For 2008 Q4, the average fall for the six indices was 11.8 percent, but the range was about 10 percentage points: falls of 16.2 for Halifax and only 6.3 percent for Rightmove.

Figure 2 shows the more volatile quarter-on-quarter changes, though for 63 percent of the periods all series changes in the same direction and for 85 percent of the periods only one showed a different direction of change. Yet the magnitudes of the difference are often substantial, especially for the Rightmove. For example, Rightmove showed a 2009 Q2/Q1 quarterly fall of 1.1 percent when all other indices showed increases averaging 3.5 percent. The coincidence of the trends and turning points suggests either some commonality in measurement or, in these respects, measurement differences don’t matter.

Are there commonalities in measurement?
Land Registry and AcadHPI are both based on the same data and it can be seen from Figure 2






that they do track each other more closely. They both record the price “on completion,” that is, the price returned to the Land Registry as part of the legal process of registering the completed sale. They are comprehensive in their coverage of transactions,[4] at least for England and Wales. There is a need to control for the changing mix in the quality of houses sold. More expensive houses may be sold one month leading us to think average prices have increased when this may not be the case. The repeat sales methodology employed by Land Registry constrains its coverage to properties transacted more than once, so that an average of the price changes of the same houses is compiled.[5] The prices of like are, broadly speaking, compared with like, at the cost of using a more limited sample and a selection bias. AcadHPI uses the Land Registry’s entire transaction database. Its mechanism of adjusting for the differential quality mix is to weight strata categorized by property type and location.[6] The weights are transactions-based relative quantities between January 2000 and December 2003. Land Registry is implicitly weighted by the relative number of repeat transactions in the sample.

 

Nationwide and Halifax include prices for properties for which they are the mortgagee. The DCLG index covers all transactions bought with a mortgage issued by one of about 50 lenders (about 55 percent of mortgage transactions—more than Nationwide and Halifax) reporting to the Regulated Mortgage Survey of the Council of Mortgage Lenders. All three indices cover transactions in the United Kingdom but, unlike the Land Registry-based indices, exclude cash sales—about 25 percent of all purchase. While the lender-based source data have some similarities, there is much in their construction that differs. 

 

Nationwide and Halifax are based on the asking price when a mortgage is first approved—when the property is under offer, that is later than when first advertised but prior to completion. Not all approved applications will go through to completion.  DCLG is based on transaction prices “on completion.” DCLG is a transaction value-weighted[7] average of individual stratum indices with weights updated annually based on a three year moving average. Nationwide and Halifax are complied as stock quantity-weighted averages of the strata. Weights for Nationwide are updated every two years based on four year moving averages of data, while Halifax uses constant weights from 1983. All three indices use hedonic regressions to minimize the effect of changes in the quality-mix on price measurement, though the specifications of the regressions differ.[8] DCLG strata are both valued and defined by quality (price-determining) characteristics of properties estimated from hedonic regressions. As an example, DCLG include in their regression variables relating to location (local authority district or London borough), property type (PT), type of neighborhood (using the ACORN classification), local authority cluster membership, defined by the Office for National Statistics, number of habitable rooms, old or new (New), first-time buyer or former owner-occupier (FT), plus interaction terms for ACORN and PT, ACORN and FT, and PT and New. Each combination of the variables in the regression forms a stratum defined by the combinations of characteristics of the property, about 1,000 property types/strata. Halifax and Nationwide define “typical” properties by fixed characteristic sets and value them over time using the estimated coefficients of hedonic regression equations.

In spite of these quite substantial differences, the indices can be seen to track the broad phenomena of the trend and turning points in residential property prices. Interestingly, there is no more commonality between mortgage-based indices than those based on Land Registry data.

Rightmove advertises properties for sale online throughout the United Kingdom covering asking prices of the 90 percent of estate agents stated to advertise on their site. Properties that do not sell are also included. The index is compiled from the asking prices of properties, the prices at the very beginning of the buying and selling process. Weights (and mix-adjustment) are according to the stock of properties in terms of geographical distribution and property type.  Rightmove is distinct in its use of asking price and has the least commonality with other indices.[9]


Do the commonalities in measurement matter?

Three main points are apparent:

  • Similar trends and turning points are tracked by all indices in spite of their data and methodological differences, yet substantial differences remain especially at some peaks and troughs, and more so for quarter-on-quarter comparisons.

  • The use of quarterly annual changes in Figure 1 does much to smooth the discrepancies endemic in the quarter-on-quarter indices in Figure 2.

  • Indices using similar source data seem to move more closely together, even though the coverage of the data and methodology may vary considerably. The correspondence between the Land Registry and AcadHPi is the most striking, being based on the same data but having very different coverage, weighting, and methodologies for controlling for quality mix.

But these points are based on a single country’s experience. Consider a further example, the United States.



House price indices for the United States
The United States has two main indices for residential property prices, the Federal Housing Finance Agency (FHFA)[10] “purchases only” house price index and the S&P/Case-Shiller National Home Price Index. The FHFA produces an “all transactions” HPI that includes refinance appraisals that are not sales that comprise nearly 90 percent (about 35 million of the 40 million repeat transactions). FHFA itself notes evidence that prices based on appraisals submitted for refinancing tend to lag market trends and have an appraisal bias. The “purchases-only” HPI excludes refinancing transactions. Leventis (2008) estimates that removing appraisals accounts for 1.54 percentage points of FHFAs 4.27 percent average difference over Case-Shiller for the four-quarter price change estimates over 2006Q3-2007Q3for the ten original MAs. Both FHFA and Case-Shiller use the same repeat-sales methodology to control for quality changes in the mix of houses sold. They have the same coverage of type of properties; that is, they include transactions on one-family houses and exclude 2- to 4-family houses, condominiums and cooperatives, and weight changes in regional price indices over 9 US census divisions.

The FHFA, Case-Shiller and Census Bureau indices do not incorporate Condominiums.  However, in November 2008, Standard & Poor's launched indices designed to track condominium prices in five major metropolitan areas—Boston, Chicago, Los Angeles, New York and San Francisco.  The National Association of Realtors provides median values (by quarter) for a larger number of cities for condominium prices, but these are not quality adjusted.


Different movements

Figure 3 shows the Census Bureau’s index to be quite different from FHFA and Case-Shiller, though this is to be expected since its coverage is of new houses only.

  • What is striking from Figure 3 is the different timing of the downturn in house prices: Annual changes in Case-Shiller turn negative in 2006 Q4 in Figure 3, but FHFA turns negative a full year later in 2007 Q4. Figure 4 shows quarterly changes in Case–Shiller to turn negative in 2006 Q3, while FHFA dips into a negative change in 2006 Q4 to subsequently have positive changes for the next two quarters to then turn negative in 2007 Q3.

  • The difference in the magnitude of the 2008/09 downturn is also striking. Figure 3 shows for 2008 Q4 and 2009 Q1 Case-Shiller registering annual falls of over 18 percent compared with falls of around 7–8 percent for FHFA; similar discrepancies are apparent from Figure 4. 

  • Even the nature of the differences cannot be relied upon. From Figure 3, up to 2006 Q2 Case-Shiller exceeded FHFA, this being reversed between 2006 Q3 to 2009 Q4, and reversed again from 2010 Q1.

  • Figure 4 shows highly volatile quarter-on-quarter changes. Yet in spite of methodological differences, the  peaks and troughs of Case-Shiller and FHFA roughly coincide—peaking in Q2—though their amplitudes differ. 







Differences in methods
Differences in the indices are to be expected. While both indices use repeat sales methodology and cover the same type of houses, the coverage, weighting, and implementation of the repeat sales methodology differ.On coverage, the Case-Shiller National Home Price Index is based on publicly available transaction sales prices from local recording offices while the FHFA index is based on data on conventional, conforming mortgage transactions obtained from Fannie Mae and Freddie Mac. The “conforming” loan limit for mortgages is a capped and FHFA data are biased against houses purchased with relatively “high” or “low” mortgages.[11] The Case-Shiller National HPI does not have valuation data from 13 states while FHFA’s index uses data from all states (363 metropolitan areas).On weighting, the FHFA HPIs is a geometrically-weighted average of price changes of the nine census division; the weights are the relative number of one-family housing units. Case-Shiller is an arithmetically-weighted average of price changes; the weights are the relative dollar value of one-family housing units.  For example, the Case-Shiller index places a 22 percent weight on the Pacific division in contrast to the 14 percent weight of the FHFA HPI, due to the relatively higher house prices in California. The weights used for aggregating both indices are estimated using US Census data, updated every ten years, that is, in 1990, 2000, and 2010, though linear interpolations are used by FHFA to chain-weight the indices retrospectively once the subsequent benchmark census results are available.On the implementation of the repeat sales methodology, sales pairs with longer time intervals are given less weight than sales pairs with shorter intervals[12] The down-weighting for lengthy intervals used for Case-Shiller National HPI is more modest than that used by FHFA.Studies undertaken by FHFA economists on why the two indices differ find the most important reasons are the non-coverage by FHFA of “low and moderate-priced” sales, somewhat offset by the non-coverage of “high-priced” sales, and differences in down-weighting long-intervals and filters used exclude non-arms-length sales.

House price indices for Russia
Difference in RPPIs might well be expected for new versus existing properties. The evidence of Figures 3 for the US bears this out with, prior to 2005, the Case-Shiller showing inflation for existing dwellings as about double that shown by the Census Bureau’s index for new dwelling. The relative positions were reversed from 2006 with inflation for new property prices below that of existing properties, and substantially so: for the end of 2008 and early 2009, the Case-Shiller index showed property price inflation to be three times lower than the Census Bureau’s estimate for new dwellings.

 

We can of course look to further evidence from other countries for the effects of differences in estimates of boom and busts using differing indices of house prices. There exists for Russia a national price index for both new and existing dwellings. Figure 5 shows that while the differences can be sizable in absolute terms, about 15 percentage points in 2006 Q3, the two series broadly track each other.

Information on coverage and methodology for these indexes is less readily available,[13] at least to the author, than for the US and UK, but appear to be comparable. Both are produced by the national statistical office, cover apartments in urban areas, and are measured as average prices (rubles) of properties subject to market transactions. Qualitative and quantitative characteristics including location are stated as being taken into account at the time of valuation, but it is not immediately apparent how this is done. Both the average prices for new and existing dwellings are compiled for the Russian Federation from average prices of Federal districts, weighted by some combination of population and number of apartments commissioned. 

 


 

What to do?
While in spite of major coverage and measurement differences, it seems that RPPIs can track each other both in terms of trend and turning points, such differences can also give rise to sizable discrepancies. There is a natural question of what should be done given an unsatisfactory situation of competing measures with different outcomes. Possibilities include:

Do nothing. Since many indices are produced by private organizations this may be the only practical alternative. House price indices sink or swim on their own merits. It can be argued that different indices have different virtues and it is for users to choose between/reconcile them: Rightmove, for example, may be based on asking prices, but it is an earlier indicator than indices based on transaction prices. Yet there are complexities in the way house price indices are calculated and supporting documentation, especially from some private organizations, is flimsy.  Lay users may not be in a position to attempt any reasonable understanding of the differences between the measures.

Identify an official series. A recent report by the UK National Statistician recommended: “A single definitive house price index and accompanying statistics should be produced by the official statistics producer community.” and “..a regular official statistics report should be developed presenting and analysing official house price measures and their relationship to other non-official sources and wider housing market indicators.” UK GSS (2010, Recommendations 1, paragraph 1.8, and 2, paragraph 1.11, respectively). A regular report analyzing why any differences exist would indeed be useful, though the unambiguous determination of a definitive index may be problematic.

Develop standards. A possible ray of sunshine is the proposed publication in late 2011 of international guidelines on residential property price index methodology: the draft Handbook on Residential Property Price Indexes (Eurostat, 2011), though see also initiatives for house price indices for the euro area and European Union (Eurostat (2010a and 2010b). The handbook provides an excellent account of methodological issues in compiling a RPPI and associated recommendations. Yet, while a naturally welcome and valuable step, publication of standards does not of course assure their implementation.  The coverage, types of price data, quality-adjustment procedure, nature and frequency of weighting are for a large part dictated by the nature of source data available.

A complicating issue is that house price indices by private organizations such as realtors and lenders serve to advertise their business. The available information on their methodology is generally not up to the standard of a statistical office, and for some users, there will always be skepticism as to conflicts of interest, whether justified or not.  Even if the source data/methods of the private agencies do not meet the standards of the international guidelines, private organizations are unlikely to abandon their indices.

 Annex 1: House price series
Many of the residential property price indexes used in this study have been drawn from the Bank for International Settlements’ (BIS) database of property price indexes available at: http://www.bis.org/statistics/pp.htm. The codes cited below alongside “BIS” refer to this database. Use of the database requires a citation of the appropriate national source as noted at:  Link and given below along with the websites used.

The BIS country series have been supplemented by further residential property price indexes, not always published, from the national sources indicated below.

Russia: BIS: Q:RU:9:1:1:1:1:0 and Q:RU:9:1:2:1:1:0; Indices of Prices in Primary/Secondary Market of Dwellings; original source: Federal State Statistics Service: Link.

United Kingdom: BIS: Q:GB:3:1:0:2:0:0; Halifax House Price Index; original source and further series: Halifax Research:   Link

(historical house price data). BIS: Q:GB:0:1:2:1:0:0; Communities and Local Government House Price Index; original source and further series: Department of Communities and Local Government, available at: Link. Also available from UK (Office for) National Statistics at: Link. (© Crown copyright 2008 Land Registry). Acadametrics; LSL Property Services/Acadametrics House Price Index; source:  Link. Land Registry; House Price Index; source: Link. Nationwide; Nationwide House Price Index; source: Link. Rightmove; House Price Index; source: Link.

United States: BIS: Q:US:0:2:2:1:0:0; US Census Bureau; Constant Quality (Laspeyres) Price Index of New One-Family Houses Sold; original source: Link. Federal Housing Finance Agency (FHFA); FHFA “Purchases-Only” House price index; source:  Link. Standard & Poor’s; S&P/Case-Shiller National Home Price Index; source: Link.



References
Carless, Emily (2011), “Reviewing Residential Property Price Indices in the UK.” paper presented at the Workshop on Residential Property Price Indices, Statistics Netherlands, The Hague, 10-11 February 201. Available at:


Link.

 

Claessens, Stijn, M. Ayhan Kose, and Marco E. Terrones, 2008, “What Happens During Recessions, Crunches and Busts?” IMF Working Paper 08/274 (Washington: International Monetary Fund).

Dey-Chowdhury, Sumit  (2007) “House Price Indices of the UK – Methods Explained,” UK Office for National Statistics, Economic & Labour Market Review, 1,1, January. Link.

 

Diewert, W. Erwin (2004), “The Treatment of Owner Occupied Housing and Other Durables in a Consumer Price Index.” In W.E. Diewert, J. Greenless and C. Hulten (eds.), Price Index Concepts and Measurement, NBER Studies in Income and Wealth, University of Chicago Press.

 

Eurostat, (2010a), European Commission, Directorate G: Business Statistics, Unit G-6: Price Statistics; Purchasing Power Parities, Research Paper, Experimental House Price Indices for the Euro Area and the European Union, December. Available at: Link.

 

Eurostat, (2010b), European Commission, Draft Technical Manual on Owner-Occupied Housing for the Harmonised Index of Consumer Prices, v.1.9, February. Available at: Link.

 

Eurostat (2011), European Commission, Directorate G: Business Statistics, Unit G-6: Price Statistics; Purchasing Power Parities, draft Handbook on Residential Property Price Indices. Available at:

Link.

 

Fenwick, David (2006), “Statistics on Real Estate Prices: The Need for a Strategic Approach.” Paper presented at the OECD-IMF Workshop on Real Estate Price Indexes, November 6-7, OECD: Paris. Available at:

Link.

 

Hill, Robert (2011), “Hedonic Price Indexes for Housing,” OECD Statistics Directorate Working Paper 35, STD/DOC(2011)1/REV1, February.

 

Leventis, Andrew 2008, “Revisiting the Differences between the OFHEO and S&P/Case-Shiller House Price Indexes: New Explanations,” Office of Federal Housing Enterprise Oversight, January, Available at: www.ofheo.gov/media/research/OFHEOSPCS12008.pdf .

 

Mason, Phil and Gwilym Pryce (2011), “Controlling for Transactions Bias in Regional House Price Indices,” forthcoming, Housing Studies.

 

Meissner, Chris and Stephen Satchell (2010), “A Comparison of the Case-Shiller House Price Index Methodology with the Academetrics House Price index Methodology,” Acadametrics Limited, December. Available at:

www.acadametrics.co.uk/Meissner%20Satchell.pdf.

 

Reinhart, Carmen M. and Kenneth S. Rogoff, 2009, “This Time is Different, Eight Centuries of Financial Folly,” Princeton University Press, Princeton and Oxford.

Shiller, R.J., (1991). “Arithmetic Repeat Sales Price Estimators,” Journal Housing Economics, 1, 1, 110–126.

 

U.K. Government Statistical Service (GSS), (2010), National Statistician's Review of House Price Statistics, London: GSS, December. Available at: Link.

 

Wood, Robert (2005), “A Comparison of UK Residential House Price Indices.” In Real Estate Indicators and Financial Stability, Bank of International Settlements Papers 21, 212–217. Proceedings of a joint Conference Organized by the BIS and IMF in Washington DC 27-28 October, 2003.

[1] The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management.

[2] Available at: Link.

 

[3] Claessens, Kose, and Terrones (2008, page 25)

[4] There remains a sample selection bias if the indices are used to represent price changes of the stock of houses (Mason and Pryce, 2011).

 

[5] The Land Registry data is a record of all residential property transactions made in England and Wales since January 1995. At the time of writing it contained details on over 15 million sales. Of these, just over five million were identifiable matched pairs. http://www.landreg.gov.uk/kb/Default.asp?ToDo=view&questId=344&catId=32.

[6]  Due to delays in processing Land Registry (LR) data, the AcadHPI results are not termed “final” until a significant volume of LR data is available which is normally after three months have passed.  AcadHPI forecast results makes use of Halifax, Nationwide, and DCLG indices. One month after any given month, LR provides average house prices based upon circa 70% of the eventual total transactions, which are used to replace the AcadHPI “forecast” result with an AcadHPI "updated" result. A further month later, LR provides prices based upon circa 90% of the eventual total transactions which are used to replace the first with a second AcadHPI "updated" result. Three months after any given month, LR provides prices based upon circa 95% of the total transactions for the month. Taking the current month as month T, in month T + 4 the AcadHPI results are regarded as sufficiently updated to be described as the AcadHPI “final” index (Meissner and Satchell, 2010, page 14).

[7] Fixed quantity baskets are applied to estimated prices in the months compared yielding a value-weighted index of price changes.

[8] See Dey-Chowdhury, 2007.

[9] At least in terms of its correlation. The correlation coefficient between Rightmove and each of Halifax, DCLF, Land Registry, AcadHPI, and Nationwide are, respectively, 0.71, 0.55, 0.68, 0.62, and 0.73. No other correlation coefficient for comparison between these series has a lower correlation coefficient. Land Registry and AcadHPI, based on the same source data, has a correlation of 0.98.

[10] The Federal Housing Finance Agency regulates Fannie Mae, Freddie Mac and the 12 Federal Home Loan Banks.

[11] The upper end is also not fully represented both because such transactions are less likely to use conventional mortgage loans, and because the size of the associated mortgages can lie above the conforming loan limits (loan amount restrictions) in the agencies. However, the study found that the bias due to this was limited.

 

[12] This procedure is well justified when phrased as a correction for heteroskedastic error variances as greater noise accompanies ratios over longer periods. The correction reduces the error but does not increase bias.

[13] See Link.

 
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