# Making repeat-sales indexes

library(rsmatrix)
library(Matrix)

The repeat-sales method is an approach to construct property price indexes by exploiting multiple sales for the same property over time to control for time-invariant differences in quality between properties. In practice, repeat-sales indexes come in a number of different flavors. There are two broad classes of repeat-sales price indexes—the geometric repeat-sales index (GRS index) and the arithmetic repeat-sales index (ARS index)—along with various weighting schemes that can be used to weight the prices in the index calculation. In all cases these indexes can be calculated as a linear estimator using a few different matrices. The purpose of the rsmatrix package is to make it easy to construct these matrices and compute a repeat-sales index.

## Some data

Let’s start with some fictitious data on house sales for five cities over a ten-year period. These data won’t make an interesting price index, but they’re broadly representative of the type of data used to make a housing price index in practice.

set.seed(15243)

periods <- seq(as.Date("2010-01-01"), as.Date("2019-12-31"), "day")

prices <- data.frame(
sale = sample(periods, 5e5, TRUE),
property = factor(sprintf("%05d", sample(1:5e4, 5e5, TRUE))),
city = factor(sample(1:5, 1e5, TRUE)),
price = round(rlnorm(5e5) * 5e5, -3)
)

prices <- prices[order(prices$city, prices$property, prices$sale), ] row.names(prices) <- NULL head(prices) #> sale property city price #> 1 2012-11-18 00001 1 2831000 #> 2 2018-10-05 00001 1 290000 #> 3 2010-09-20 00002 1 1519000 #> 4 2019-12-19 00002 1 269000 #> 5 2012-06-25 00003 1 712000 #> 6 2016-10-15 00003 1 520000 ## Preparing to make the matrices The repeat-sales method uses multiple sales for the same property over time to identify the change in housing prices. The major step to prepare the data for this method is to make pairs of consecutive sales (sales pairs). Note that this will remove the sizable chunk of properties for which there is only one sale. interaction(prices$city, prices$property, drop = TRUE) |> tabulate() |> quantile() #> 0% 25% 50% 75% 100% #> 1 1 2 3 11 We’ll be making a monthly index, and it’s useful to turn sale dates into year months before constructing sales pairs. (Using, say, the yearmon class from zoo instead of a factor means this can be done after making sales pairs.) prices$period <- cut(prices$sale, "month") The rs_pairs() function gives the position of the previous sale for each property in each city in the data. sales_pairs <- rs_pairs(prices$sale, interaction(prices$city, prices$property))
prices[c("price_prev", "period_prev")] <- prices[sales_pairs, c("price", "period")]

#>         sale property city   price     period price_prev period_prev
#> 1 2012-11-18    00001    1 2831000 2012-11-01    2831000  2012-11-01
#> 2 2018-10-05    00001    1  290000 2018-10-01    2831000  2012-11-01
#> 3 2010-09-20    00002    1 1519000 2010-09-01    1519000  2010-09-01
#> 4 2019-12-19    00002    1  269000 2019-12-01    1519000  2010-09-01
#> 5 2012-06-25    00003    1  712000 2012-06-01     712000  2012-06-01
#> 6 2016-10-15    00003    1  520000 2016-10-01     712000  2012-06-01

Now that the data are oriented as sales pairs, we can remove pairs of transactions that don’t belong in the calculation. The goal is to help motivate the assumption that it is only time-invariant differences in quality that confound a change in housing prices with a change in the composition of housing that sells over time.

It’s normal to remove pairs of transactions that are close together (e.g., less than two months apart), as these properties may be flips or the result of financial difficulty that is not indicative of what the property would have otherwise sold for. Note that this filters out properties that only sell once.

prices$holding_period <- with(prices, as.numeric(period) - as.numeric(period_prev)) prices <- subset(prices, holding_period > 2) Properties with a very large change in price may have undergone major improvements or depreciation that similarly undermine the assumptions of the repeat-sales method. There are many ways to try and filter out these transactions, but a simple approach is to remove pairs of sales where the monthly return for a property is greater than, say, 2.5 median absolute deviations from the median. (The gpindex package has several other methods for dealing with extreme changes in prices.) library(gpindex) monthly_return <- with(prices, (price / price_prev)^(1 / holding_period)) robust_z <- grouped(robust_z) prices <- subset(prices, !robust_z(monthly_return, group = city)) head(prices) #> sale property city price period price_prev period_prev #> 2 2018-10-05 00001 1 290000 2018-10-01 2831000 2012-11-01 #> 4 2019-12-19 00002 1 269000 2019-12-01 1519000 2010-09-01 #> 6 2016-10-15 00003 1 520000 2016-10-01 712000 2012-06-01 #> 8 2011-07-18 00004 1 305000 2011-07-01 90000 2010-07-01 #> 9 2013-12-03 00004 1 768000 2013-12-01 305000 2011-07-01 #> 10 2018-08-02 00004 1 121000 2018-08-01 768000 2013-12-01 #> holding_period #> 2 71 #> 4 111 #> 6 52 #> 8 12 #> 9 29 #> 10 56 ## Calculating a repeat-sales index With the data in the right form, and potentially problematic transactions gone, we can now make the repeat-sales matrices and calculate a housing price index. The strategy is to make a constructor function based on the sales-pair data that can be used to make the repeat-sales matrices. These matrices are naturally spare, and the volume of transactions means we’ll benefit for using sparse matrices. matrices <- with( prices, rs_matrix(period, period_prev, price, price_prev, city, sparse = TRUE) ) The simplest repeat-sales index in the geometric repeat sales (GRS) index.1 This index uses the $$Z$$ and $$y$$ matrices. Z <- matrices("Z") y <- matrices("y") grs <- exp(solve(crossprod(Z), crossprod(Z, y))) head(grs) #> 6 x 1 Matrix of class "dgeMatrix" #> [,1] #> 1.2010-02-01 0.9319471 #> 2.2010-02-01 1.0682105 #> 3.2010-02-01 1.0434833 #> 4.2010-02-01 1.0185787 #> 5.2010-02-01 0.9715417 #> 1.2010-03-01 1.0499673 There are various inverse-variance (or interval) weighting schemes found in the literature. These weights are the result of regressing the residuals from the GRS model against a function of the holding period. The most well-known of these weights, the Case-Shiller weights, model variance as a linear function of holding period. grs_resid <- y - Z %*% log(grs) mdl <- lm(as.numeric(grs_resid)^2 ~ prices$holding_period)
W <- Diagonal(x = 1 / fitted.values(mdl))

grs_cs <- exp(solve(crossprod(Z, W %*% Z), crossprod(Z, W %*% y)))
#> 6 x 1 Matrix of class "dgeMatrix"
#>                   [,1]
#> 1.2010-02-01 0.9216778
#> 2.2010-02-01 1.0395309
#> 3.2010-02-01 1.0265610
#> 4.2010-02-01 0.9885539
#> 5.2010-02-01 0.9611430
#> 1.2010-03-01 1.0373087

Adding the square of the holding period to the model for the variance, with or without an intercept, is a common variation for the interval weights. These weights can all be extended to account for the same house selling more than twice.

Another type of repeat-sales index is the arithmetic repeat sales (ARS) index. This index uses the $$Z$$, $$X$$, and $$Y$$ matrices.

X <- matrices("X")
Y <- matrices("Y")

ars <- 1 / solve(crossprod(Z, X), crossprod(Z, Y))
#> 6 x 1 Matrix of class "dgeMatrix"
#>                   [,1]
#> 1.2010-02-01 0.9049071
#> 2.2010-02-01 1.1567195
#> 3.2010-02-01 1.0093530
#> 4.2010-02-01 1.0107540
#> 5.2010-02-01 0.9458214
#> 1.2010-03-01 0.9840927

Like the GRS index, the ARS index can be calculated with inverse-variance weights.

ars_resid <- Y - X %*% (1 / ars)

mdl <- lm(as.numeric(ars_resid)^2 ~ prices$holding_period) W <- Diagonal(x = 1 / fitted.values(mdl)) ars_cs <- 1 / solve(crossprod(Z, W %*% X), crossprod(Z, W %*% Y)) head(ars_cs) #> 6 x 1 Matrix of class "dgeMatrix" #> [,1] #> 1.2010-02-01 0.9040179 #> 2.2010-02-01 1.1194659 #> 3.2010-02-01 0.9667189 #> 4.2010-02-01 0.9549480 #> 5.2010-02-01 0.9161033 #> 1.2010-03-01 0.9856805 Dividing the $$X$$ and $$Y$$ matrices by the price of the first sale for each row produces an equally-weighted arithmetic index (as opposed to value-weighted index). ars_ew <- with( prices, 1 / solve(crossprod(Z, X / price_prev), crossprod(Z, Y / price_prev)) ) head(ars_ew) #> 6 x 1 Matrix of class "dgeMatrix" #> [,1] #> 1.2010-02-01 0.9831875 #> 2.2010-02-01 1.1454796 #> 3.2010-02-01 1.1332362 #> 4.2010-02-01 0.9957358 #> 5.2010-02-01 0.8859613 #> 1.2010-03-01 0.9510087 Once the index is calculated, it’s often easier to turn into a matrix-like object with cities are rows and time periods as columns. The easiest way to do this is with piar. library(piar) dimensions <- do.call(rbind, strsplit(rownames(grs), ".", fixed = TRUE)) grs_piar <- elemental_index(grs, dimensions[, 2], dimensions[, 1], chainable = FALSE ) head(grs_piar, c(5, 5)) #> Fixed-base price index for 5 levels over 5 time periods #> 2010-02-01 2010-03-01 2010-04-01 2010-05-01 2010-06-01 #> 1 0.9319471 1.049967 1.0312130 0.9332345 1.0074811 #> 2 1.0682105 0.984812 1.0840525 1.0113186 0.9645851 #> 3 1.0434833 1.044671 0.9526141 0.9330665 1.0234300 #> 4 1.0185787 1.048738 1.0757727 1.1258297 1.0099260 #> 5 0.9715417 1.025133 1.0648382 1.0387825 1.0930171 ## Sales pair contributions Repeat-sales indexes have a simple interpretation as a type of efficient back-price imputation. It’s best to include the base period of the index prior to doing such an imputation. grs <- c(setNames(rep(1, 5), paste(1:5, "2010-01-01", sep = ".")), grs[, 1]) ars <- c(setNames(rep(1, 5), paste(1:5, "2010-01-01", sep = ".")), ars[, 1]) The GRS index at a point in time is simply the geometric mean of imputed price relatives. This makes it easy to calculate the percent-change contribution of each property at each point in time. grs_contributions <- Map( \(df, df_prev) { impute_back <- with(df, price_prev / grs[paste(city, period_prev, sep = ".")]) names(impute_back) <- row.names(df) impute_forward <- with(df_prev, price / grs[paste(city, period, sep = ".")]) names(impute_forward) <- row.names(df_prev) geometric_contributions( c(df$price / impute_back, df_prev$price_prev / impute_forward) ) }, split(prices, interaction(prices$city, prices$period)), split(prices, interaction(prices$city, prices$period_prev)) ) all.equal(sapply(grs_contributions, sum) + 1, grs) #> [1] TRUE range(unlist(grs_contributions)) #> [1] -0.008215347 0.009111019 The same applies to the ARS index, noting that it is a weighted arithmetic mean of imputed price relatives. ars_contributions <- Map( \(df, df_prev) { impute_back <- with(df, price_prev / ars[paste(city, period_prev, sep = ".")]) names(impute_back) <- row.names(df) impute_forward <- with(df_prev, price / ars[paste(city, period, sep = ".")]) names(impute_forward) <- row.names(df_prev) arithmetic_contributions( c(df$price / impute_back, df_prev$price_prev / impute_forward), c(impute_back, impute_forward) ) }, split(prices, interaction(prices$city, prices$period)), split(prices, interaction(prices$city, prices\$period_prev))
)

all.equal(sapply(ars_contributions, sum) + 1, ars)
#> [1] TRUE

range(unlist(ars_contributions))
#> [1] -0.07041332  0.08316015

1. The GRS index is simply the first-difference estimator for the linear model of log price with property fixed effects. This is easy enough to compute with a panel-data package like plm, but the repeat-sales matrices are more convenient when working with sales pairs.↩︎