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S&P Case-Shiller US Home Price Index – Definition

S&P CoreLogic Case-Shiller National Home Price Index Definition

The S&P/ CoreLogic Case-Shiller Home Price Indexes -also known simply as the Case-Shiller Home Price Indexes – refers to a group of indexes that measures or tracks the average changes in the prices of single-family-detached residences (houses) throughout the United States by observing the purchase price and resale value of those houses that have undergone at least two arm’s length sale transactions.

In real estate, an arm’s length sale transactions refers to a  business deal where parties involved have no previous relationship prior to the sale and exchange of the underlying property. This types of transaction is free from influence and the home being sold is likely to attract a fair market value and therefore reliable in tabulating data.

Consequently, transactions involving family members or companies with related shareholders (Subsidiaries) are not considered arm’s length transactions. This is because it’s highly unlikely that a transaction involving such a group would yield a close fair market value sale compared to a deal between strangers. A business deal involving parties with pre-existing relationship are known as arm-in-arm transactions.

Also, apart from having two or more recorded arms-length sale transactions, for a house to be eligible to be included in the indices it must be considered a single-family dwelling. In addition, transactions following a House designation change to let’s say a condominium or a two-bedroom house remodeled to a five-bedroom house would not be eligible. Home with unrealistic data in terms of valuation are considered erroneous and are therefore excluded.

A Little More on What is the S&P CoreLogic Case-Shiller National Home Price Index

S&P CoreLogic Case-Shiller Home Price Indexes are designed to be a reliable and consistent benchmark that measure increases or decreases in the market value of residential real estate in defined regions and the overall housing prices in the United States. To understand this leading index measure better we can explain it by breaking down its full name to individual components;

It is common to come across S&P 500 or S&P Global in the newspaper or watch it in the news, but what is S&P in full? Well, Standard & Poor’s is a business intelligence company that provides data rating information that guides investors. The name combinations “Standard and Poor” are the names of the two financial companies that merged in 1941. S&P has been a partner company since 2006 and has been distributing and charging clients for the Case-Shiller franchised home price indexes.

CoreLogic Inc, just like Standard & Poor’s is a business intelligence company and the current owner of the Case Shiller Weiss business methodology and techniques for measuring price changes for residential houses in the United States. The company acquired the Case Shiller Weiss business model from Fiserv in 2013. The Analysts at CoreLogic as well as S&P are the ones that produce the ratings quarterly based on the data from local government assessor and records offices to get the valuation of homes.

In the 1980s, during the housing price boom in Boston, Economists Karl Case and Robert Shiller in an effort to study home pricing trends in the United States developed a method for comparing repeat sales of the same homes. That methodology is what is now commonly referred to as the Case–Shiller home price index.

Allan Weiss a fellow economist encourage the duo to form a company with intention of commercializing the information.  The Case Shiller Weiss Company was formed with Mr. Weiss as the CEO up to its acquisition by Fiserv in 2002 and subsequently an acquisition by CoreLogic.

Notably, MacroMarkets LLC also holds license and sublicensing rights to the S&P/Case-Shiller Home Price Indices. The company was formed in 1991 and interestingly is owned by Allan Weiss and Robert Shiller.  The company designs and develops financial instruments that provide investment and risk management services.

A housing price index is a tracking of changes in home prices in a particular region over a period of time. The start date of tracking the house is given a housing price index of 100 and then rises with repeated sales of the home over time, whereas a twelve month change is usually indicated in percentage form.

There are several methodologies commonly used to calculate housing price indexes such as;

  • The median price index that mealy tracks the price of houses  sold over time
  • The Hedonic regression that looks at characteristics like the  house square footage, number of bedrooms or distance from city center or amenities to determine price.
  • Simple moving average that involves a monthly calculation of housing indexes using an algorithm that matches the new sale price to the first sale price.
  • Repeat sales methodology which measures price changes of the same house between a previous and current sale over a period of months or years. The difference in the value of this sale pair is measured and recorded.

The S&P CoreLogic Case-Shiller Home Price Indexes uses the repeat sales methodology in an attempt to gauge appreciation or depreciation of all housing in a particular geographical market. However, the repeat sale methodology is not perfect, as some housing units will have been improved between sales, and some will have deteriorated.

Nevertheless, it is far superior compared to other methodology such as the National Association of Realtors (NAR) Median Index or the Census Bureau Median Index that simply looks at all sales prices. Admittedly, such methods are flawed in their calculations since they don’t consider structural changes to homes or other market factors.

Repeat-Sales Method

Apart from Case-Shiller Home index, other home price index publishers including the Federal Housing Finance Agency’s (FHFA) monthly House Price Index use the repeat-sales methodology to tabulate their data. The data is sold to stakeholders in housing finance industries such as potential homebuyers as well as sellers, investors, among others.

The reason why most index publishers have largely adopted the repeat-sale method is because changes in houses are calculated based on sales regarding the same property. Therefore, the problem of trying to account for prices in different homes with varying characteristics is avoided. In addition, the methodology is value-weighted, meaning price trends for higher valued homes tend to have more of an impact on the index.

However, newly constructed houses that were sold once during the reported period are considered to have one arm’s length sale transactions and despite providing meaningful indications of market activities they are not accounted for in the report. Also, homes that have deteriorated in value as well as those that have undergone major remodeling are dropped in the reports due to a need to maintain consistency.

Additionally, the repeat sale methodology requires large number of homes with two or more arm’s length sale transactions which can be problematic to find during slow economic growth periods. Further, in some cities like Texas and others, sale prices are not publicly disclosed on deed records, so it is not possible to obtain sale prices from public data sources.

Finally, there are index variances observed in tabulations that use houses with repeat-sales that have larger time intervals in between transactions –indexes tend to adjust their weight downwards.


The S&P CoreLogic Case-Shiller Home Price Indexes is not a single index measure but are multiple each classified with emphasis on particular markets. The indexes can be broadly classified into two categories as below:

  1. a)   The Quarterly indexes that cover nine major U.S Census divisions.

This index is commonly referred to as The S&P CoreLogic Case-Shiller National Home Price Index which is calculated quarterly by CoreLogic. The same is published on every last Tuesday of February, May, August and November of each year by standards and poor’s.

The nine census division covered by the National home price index includes; East North Central, East South Central, Middle Atlantic, The Mountain, New England, The Pacific, South Atlantic, West North Central, and West South Central.

  1. b)   The Monthly Indexes that cover Specific U.S Metropolitan Statistical Areas(MSA)

The monthly indices use a three-month moving average algorithm. Home sales pairs are accumulated in rolling three-month periods, on which the repeat sales methodology is applied. The index point for each reporting month is based on sales pairs found for that month and the preceding two months.

For example, the March 2019 index point is based on repeat sales data for January, February and March of 2019. This averaging methodology is used to offset delays that can occur in the flow of sales price data from county deed recorders and to keep sample sizes large enough to create meaningful price change averages.

These indexes are further grouped into three sub-categories that include:

  1. The 10-city composite index (CXSR composite index)

The ten regions used in the Composite of 10 are: Greater Boston (BOXR), Chicago (CHXR), Denver (DNXR), Las Vegas (LVXR), Greater Los Angeles (LXXR), South Florida (MIXR), New York (NYXR), San Diego (SDXR), San Francisco (SFXR), Washington DC (WDXR).

Nine of the ten are based on the Metropolitan Statistical Areas (MSAs) defined by the U.S. Office of Management and Budget. However, the New York region is expanded from the New York MSA to include counties in New York State, Connecticut, New Jersey and Pennsylvania that are within commuting distance of New York City.

  1. The 20-city composite index (SPCS20R composite index)

This includes all the above ten cities in the CXSR composite index plus ten additional regions to form a Composite of 20 cities. The additional cities include: Atlanta (ATXR), Charlotte (CRXR), Greater Cleveland (CEXR), Dallas (DAXR), Detroit (DEXR), Minneapolis (MNXR), Phoenix (PHXR), Portland (POXR), Seattle (SEXR), and Tampa (TPXR).

  1. Individual metro area indexes for each of the cities listed above.

Generally, sales pairs collected from each region is collected and weighted to control any changes in particular homes. Sales pairs are assigned weights to account for fluctuations in price that can be attributed to factors such as extensive home remodeling or extreme neglect. All the eligible and weighted sale pairs from each MSA are what forms aggregates in corresponding SPCS20R and CXSR composite indexes.

Benefits of Housing indices

A house is an important part of the American dream. Good home markets where most citizens can afford comfortable housing are a general indicator to how the economy is doing. Additionally, a home is considered investments by most citizens and forms a central part in their portfolio of assets. Therefore, Home price movements have a significant impact on the total value of most investors’ portfolios.

Case-Shiller index acts as a barometer in real estate that can influence consumer spending as well as contribution to the general GDP of the United States. For instance, during strong economic times investors will rely on housing indices to heighten their constructions activities due to expected demands for residential homes. This market is estimated to contribute close to five percent of the GDP.

The housing market influences currencies and therefore home price indices are closely monitored by forex exchange dealers. For instance, when housing data is positive to maintain growth the Central bank would increase interest rates to flush out unnecessary liquidity from the market which often leads to gain in value of a country’s currency.

In contrast, when the data is negative, in an attempt to spur growth the central bank would institute an interest cut to increase liquidity. Such a move will deter most investors since they will have minimal profits. Consequently, this results in a decline to the value of the currency of the respective country.

Lastly, financial institution use home price indexes to for planning and estimating the probability of debt repayments in terms of mortgage lending. In addition, it guides them in evaluating different markets in an attempt to customize mortgages for different geographical location based on such localities indices.

Trading with the Case-Shiller Indexes

The Case-Shiller Home Price indexes are used as the underlying pricing mechanism in S&P/Case-Shiller Home Price (CSI) Indices futures and options. These instruments are traded at the Chicago Mercantile Exchange (CME), which is also known as the Chicago Merc. However, CSI futures and options indices have a position limit of 5,000 contracts.

CME real estate futures and options are cash-settled to a weighted composite index of U.S. housing prices in 10 major U.S. regions. The Case-Shiller Composite Index (Ticker symbol: CUS)  includes ;Boston (BOS), Chicago (CHI), Denver (DEN), Las Vegas (LAV), Los Angeles (LAX), Miami (MIA), New York (NYM), San Diego (SDG), San Francisco (SFR), Washington, D.C. (WDC)

Listing of contracts at the CME is guided by how long the contract extends into the future. For instance, those extending 18 months into the future are listed on a quarterly cycle in February, May, August and November; those extending 19 to 36 months are listed on a bi-annual schedule in May and November; and contracts for 37 months to 60 months are listed on an annual schedule in November.

The CSI is a comprehensive financial tool for risk management and investment that make it possible to manage U.S. housing risk and profit in either bear or bull markets.  It is also a way to make trading in real estate a short-term and liquid investment with access to a unique class of asset.

In conclusion, The S&P/Case-Shiller Home Price (CSI) Indices futures and options and the S&P CoreLogic Case-Shiller Home Price Indexes provide a unique market opportunity in terms of knowledge as well as alternative investments.

References for Case-Shiller Price Index

Academic Research on S&P/Case-Shiller U.S. Home Price Index

         Understanding recent trends in house prices and home ownership, Shiller, R. J. (2007). . National Bureau of Economic Research. The paper is authored by Mr. Robert Shiller; the inventor of the Case-Shiller home price index. Mr. Shiller tries to explain the housing boom prior to 2007 and asserts that the trend is due to psychological view by investors that a house is an important investment.

         What have they been thinking? Home buyer behavior in hot and cold markets, Case, K. E., Shiller, R. J., & Thompson, A. (2012). National Bureau of Economic Research. The paper is co-authored by both Case and Shiller, the developers of the renowned Case-Shiller housing indices. The paper presents findings from surveys in of four U.S. metropolitan areas that inquired the motive behind the home bubble and reports that the boom is due to expected price rise in future.

         S&P/CASE-SHILLER HOME PRICE INDICES 2011 YEAR IN REVIEW, Maitland, M. F., & Blitzer, D. M. (2012). The article presents a review of the S&P CoreLogic Case-Shiller U.S.  Home Price Indices as tabulated in the year 2011 with market analysis in regards to housing trends.

         Was there a US house price bubble? An econometric analysis using national and regional panel data, Clark, S. P., & Coggin, T. D. (2011). The Quarterly Review of Economics and Finance, 51(2), 189-200. The study tries to unravel whether there has been a housing bubble in the United States as frequently reported in journals. The paper presents findings that indicate an anomaly exist as previously reported and discuss the implications as well as policies regarding the collapse in the housing market.

        Unit roots and structural change: an application to US house price indices, Canarella, G., Miller, S., & Pollard, S. (2012). Urban Studies, 49(4), 757-776. The paper tries to find whether there is any validity to the Ripple effect by investigating S&P/Case–Shiller Composite10 index using a time-series analysis. The paper asserts discovery of conflicting

evidence from the analysis.


         Forecasting the US housing market, Kouwenberg, R., & Zwinkels, R. (2014). International Journal of Forecasting, 30(3), 415-425. The paper presents an econometric model  that mechanism can assign more weight to positive serial correlation in  housing boom times, and more weight to reversal to fundamental values during housing downturns.

         A hierarchical factor analysis of US housing market dynamics, Moench, E., & Ng, S. (2011). The Econometrics Journal, 14(1), C1-C24. The paper studies the U.S housing dynamics and presents findings that that lowering mortgage rates leads to increased market activities during strong economic times. Further, the paper asserts that in weak market activities factors such as investor confidence stock performance influence recovery and growth.

         Trading house price risk with existing futures contracts, Hinkelmann, C., & Swidler, S. (2008). The Journal of Real Estate Finance and Economics, 36(1), 37-52. The paper examines whether futures and options contract in real estate can be effectively used to hedge risk in residential housing following the introduction of such tradable instruments at the Chicago Mercantile Exchange (CME), also known as the Chicago Merc.

         National transaction-based land price indices, Sirmans, C. F., & Slade, B. A. (2012). The Journal of Real Estate Finance and Economics, 45(4), 829-845. The paper examines land price indices for residential, commercial, and industrial land use and uses data samples that spans over the 20 years period from 1991 to 2009. The paper compares and contrasts these indices and asserts that residential land use shows the most volatility in price.

         The boom and bust of US housing prices from various geographic perspectives, Cohen, J. P., Coughlin, C. C., & Lopez, D. A. (2012). Federal Reserve Bank of St. Louis Review, 94(5), 341-67. The paper review and summarizes the recently experienced boom and bust in the United States housing prices from various geographic markets as well as in several foreign countries. The paper uses Standard & Poor’s Case-Shiller house price index data in its analysis.

         A guide to aggregate house price measures, Rappaport, J. (2007). Economic Review-Federal Reserve Bank of Kansas City, 92(2), 41. The paper tries to give a guideline as to how the aggregate house price index works and looks at the advantages and disadvantages of selected methodology  that analyst rely on to measure the aggregate price of housing.

         House price indices: does measurement matter?, Silver, M. (2011). World Economics-Abingdon, 12(3), 69. The paper presents findings three case studies conducted in the United Kingdom, United States and the Russian Federation as to whether home price indices that are presented in the market are reliable and most importantly can they be considered in decision making considering their discrepancies.


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