165
July 2020

Residential Property Price Indexes: Spatial Coordinates
versus Neighbourhood Dummy Variables

Erwin Diewert
University of British Columbia, Department of Economics

Chihiro Shimizu
Nihon University & Center for Spatial Information Science, University of Tokyo

Residential Property Price Indexes: Spatial
Coordinates versus Neighbourhood Dummy
Variables
W. Erwin Diewert and Chihiro Shimizu,∗
July 1, 2020
Abstract
The paper addresses the following question: can satisfactory residential property priceindexes be constructed using hedonic regression techniques where location eﬀects aremodeled using local neighbourhood dummy variables or is it necessary to use spatial
coordinates to model location eﬀects. Hill and Scholz (2018)[35] addressed this question
and found, using their hedonic regression model, that it was not necessary to use spatial
coordinates to obtain satisfactory property price indexes for Sydney. However, theirhedonic regression model did not estimate separate land and structure price indexes
for residential properties. In order to construct national balance sheet estimates, it isnecessary to have separate land and structure price indexes. The present paper addresses
the Hill and Scholz question in the context of providing satisfactory residential land priceindexes. The spatial coordinate model used in the present paper is a modification ofColwell’s (1998)[7] spatial interpolation method. The modification can be viewed as a
general nonparametric method for estimating a function of two variables.
Key Words
Residential property price indexes, System of National Accounts, Balance Sheets, methods of depreciation, land and structure price indexes, hedonic regressions, spatial coordinates, bilinear interpolation methods, nonparametric methods for fitting two dimensional surfaces.
Journal of Economic Literature Classification Numbers
C2, C14, C21, C23, C25, C43, E31, R21.

1 Introduction
It is a diﬃcult task to construct constant quality price indexes for residential (and commercial)properties. Properties with structures on them consist of two main components: the land component and the structure component. The problem is that each property has a unique location∗ W. Erwin Diewert:

School of Economics, University of British Columbia, Vancouver B.C., Canada,
V6T 1Z1 and the School of Economics, University of New South Wales, Sydney, Australia (email: erwin.diewert@ubc.ca) and Chihiro Shimizu: Center for Spatial Information Science, The University ofTokyo and Nihon University (e-mail: cshimizu@csis.u-tokyo.ac.jp) . The authors thank Jan de Haan for
helpful comments and gratefully acknowledge financial support from the SSHRC of Canada and Nomura
Foundation.

1

(which aﬀects the price of the land component) and given the fact that the same property is
not sold in every period, it is diﬃcult to apply the usual matched model methodology whenconstructing constant quality price indexes. Bailey, Muth and Nourse (1963)[1] developed
the repeat sales methodology in an attempt to apply the matched model methodology to the
problem of constructing property price indexes but this methodology does not allow for theuse of single sales of the same property over the sample period and thus in particular, thesales of properties with new structures do not aﬀect the resulting indexes, which could lead tobiased indexes. Moreover, properties with structures on them do not retain the same quality
over time due to structure depreciation and renovations or additions to the structure. Thus
the matched model methodology for the construction of constant quality price indexes does
not work in the property price index context.
A possible solution to the above measurement problem is to use a hedonic regression model
approach to the construction of property price indexes.*1 This approach regresses the sale priceof a property (or the logarithm of the sale price) on various characteristics of the properties inthe sample. An important price determining characteristic of a property is its location. The
location of a property can be described by its neighbourhood (a local government area or a
postal code) or by its latitude and longitude, the spatial coordinates of the property. Most
hedonic property regressions use the former approach to describing the location of a property
but in recent years, the availability of spatial coordinate information has grown. Colwell(1998)[7] was an early pioneer in the use of spatial coordinate information in a property priceregression and more recently, Hill and Scholz (2018)[35] used spatial coordinates to model
Sydney house prices.
The main question that this paper addresses is the following one: can satisfactory residential
property price indexes be constructed using hedonic regression techniques where location effects are modeled using local neighbourhood dummy variables or is it necessary to use spatial
coordinates to model location eﬀects. Hill and Scholz (2018)[35] addressed this question andfound that it was not necessary to use spatial coordinates to obtain satisfactory property priceindexes for Sydney. However, their hedonic regression model did not estimate separate landand structure price indexes for residential properties. In order to construct national balance
sheet estimates, it is necessary to have separate land and structure price indexes. The present
paper addresses the Hill and Scholz question in the context of providing satisfactory residential
land price indexes. The spatial coordinate model used in the present paper is a modification ofColwell’s (1998)[7] spatial interpolation method. The modification can be viewed as a general
nonparametric method for estimating a function of two variables.
A basic building block in Colwell’s method is a method of bilinear interpolation over a squarethat was developed in the mathematics literature. We explain this method in section 2 below.
In section 3, we explain how this bilinear method of interpolation over a square can be extended
to a method of interpolation over a grid of squares. We then follow the example of Poirier(1976)[43] and Colwell (1998)[7] and convert the interpolation method into an econometricestimation model. The resulting method will be used in later sections to model the land priceof a property as a function of its spatial coordinates.
In section 4, we compare Colwell’s spatial coordinate model with the penalized least squares
approach used by Hill and Scholz (2018)[35] in their study of Sydney property prices. We notesome problems with the Hill and Scholz approach.
*1 For expositions of the hedonic regression approach to the construction of constant quality price indexes,

see de Haan and Diewert (2013)[10] and Hill (2013)[34].

2Section 5 describes our data on sales of residential properties in Tokyo over the 44 quarters
starting in first quarter of 2000 and ending in the last quarter of 2010. We used the same data
as we used in Diewert and Shimizu (2015a)[20] on sales of residential houses in Tokyo except
the present study added additional data on sales of residential properties with no structureson the land plot.
Section 6 sets out the builder’s model approach to hedonic property price regressions. Thisapproach uses the property’s sale price as the dependent variable and splits up property value
into the sum of the land and structure components. This additive decomposition approach
has a long history in the property hedonic regression literature but what is relatively recent isthe use of an exogenous construction cost price series to value the structure component of thedecomposition. It is this use of an exogenous index that allows us to decompose property value
into plausible land and structure components.*2 This section uses both the nonparametricspatial coordinate approach due to Colwell as well as the neighbourhood approach to model
the influence of location on land prices. We look at the resulting land price indexes as we
increase the size of the grid and we find that there is little change in these land price indexes
over a reasonable range of alternative grid sizes. Section 7 adds more characteristics to themodel and again looks at how the resulting land prices change as we add more characteristics.
Section 8 compares the overall property price indexes generated by the important models
explained in the previous sections (instead of comparing just the land price components ofresidential property sales). For comparison purposes, we also compared our “best” model
results with a “traditional” hedonic property price hedonic regression which regresses thelogarithm of property price on a linear combination of the property characteristics and time
dummy variables.*3 This traditional approach does not generate reasonable subindexes forland and structures but it can generate reasonable results for an overall property price index.
Section 9 concludes. An Appendix contains the results of selected regression models as wellas the data underlying the Figures in the main text.

2 Bilinear Interpolation on the Unit Square
Our task in this section is to explain how a particular method of bilinear interpolation works
for functions of two variables defined on the unit square. This method of interpolation is abasic building block that can be used to construct a method for approximating a functionof two variables that is defined over the unit square. Suppose that f (x, y) is a continuousfunction of two variables, x and y, where 0 ≤ x ≤ 1 and 0 ≤ y ≤ 1. Suppose that f takes on
the values γij at the corners of the unit square; i.e., we have:
γ00 ≡ f (0, 0); γ10 ≡ f (1, 0); γ01 ≡ f (0, 1); γ11 ≡ f (1, 1).

(1)

Assuming that we know (or can estimate) the heights of the function at the corners of theunit square, we look for an approximating continuous function that satisfies counterparts toequations (1) at the corners of the unit square and is a linear function along the four line
segments that make up the boundary of the unit square. Colwell (1998; 89)[7] showed that

*2 The basic idea of using an exogenous cost index can be found in Diewert (2010; 33-35)[14].

Diewert, de Haan and Hendricks (2015)[18].
*3 This traditional hedonic regression approach can be traced back to Court (1939)[8].

3

See also

the following quadratic function of x and y, g(x, y), satisfies these requirements:*4
g(x, y) ≡ γ00 (1 − x)(1 − y) + γ10 x(1 − y) + γ01 (1 − x)y + γ11 xy.

(2)Colwell (1998; 89)[7] also showed that g(x, y) is a weighted average of γ00 , γ10 , γ01 and γ11for (x, y) belonging to the unit square.*5 In order to gain more insight into the properties ofg(x, y), rewrite g(x, y) as follows:g(x, y) = γ00 + (γ10 − γ00 )x + (γ01 − γ00 )y + [(γ00 + γ11 ) − (γ01 + γ10 )]xy.

(3)

Thus if γ00 + γ11 = γ01 + γ10 , then g(x, y) is a linear function over the unit square. However,if γ00 + γ11 ̸= γ01 + γ10 , then g(x, y) is a saddle function; i.e., the determinant of the matrix ofsecond order partial derivatives of g(x, y), ∇2 g(x, y), is equal to −[(γ00 +γ11 )−(γ01 +γ10 )]2 < 0and hence ∇2 g(x, y) has one positive and one negative eigenvalue.
In the following section, we will follow the example of Colwell (1998; 91)[7] and show how thefunction g(x, y) defined over the unit square can be extended in order to define a continuousfunction over a grid of squares.

3 Bilinear Spline Interpolation over a Grid
In order to explain how Colwell’s method works over a grid of squares, we will explain his
method for the case of a 3 by 3 grid of squares. The method will be applied to the variables
X and Y that are defined over a rectangular region in X, Y space. We assume that X and Y
satisfy the following restrictions:Xmin ≤ X ≤ Xmax ; Ymin ≤ Y ≤ Ymax

(4)

where Xmin < Xmax and Ymin < Ymax . We translate and scale X and Y so that the range ofthe transformed X and Y , x and y, lie in the interval joining 0 and 3; i.e., define x and y as
follows:x ≡ 3(X − Xmin )/(Xmax − Xmin ); y ≡ 3(Y − Ymin )/(Ymax − Ymin ).
(5)
Define the following 3 dummy variable (or indicator) functions of x:
D1 (x) ≡ 1 if 0 ≤ x < 1; D1 (x) ≡ 0 if x ≥ 1;
D2 (x) ≡ 1 if 1 ≤ x < 2; D2 (x) ≡ 0 if x < 1 or x ≥ 2;

(6)

D3 (x) ≡ 1 if 2 ≤ x ≤ 3; D3 (x) ≡ 0 if x < 2.
Note that if 0 ≤ x ≤ 3, then D1 (x) + D2 (x) + D3 (x) = 1 so that the 3 dummy variable
functions sum to 1 if x lies in the interval between 0 and 3.
The above definitions can be used to define the 3 dummy variable functions of y, D1 (y), D2 (y)and D3 (y), where y replaces x in definitions (6). Finally, a set of 3 × 3 = 9 bilateral dummy
variable functions, Dij (x, y), is defined as follows:
Dij (x, y) ≡ Di (x)Dj (y);i = 1, 2, 3; j = 1, 2, 3.

(7)*4 The function g(x, y) defined by (2) is a special case of the bilinear function defined in matrix algebra

textbooks such as Mirsky (1955; 353)[41]. Poirier (1976; 61)[43] also defined the counterpart to (2) thatis defined over a rectangle. The extension of our algebra to a grid of rectangles is straightforward.
*5 It is straightforward to show that the sum of the nonnegative weights (1 − x)(1 − y), x(1 − y), (1 − x)yand xy is equal to 1. Thus g(x, y) will satisfy the inequalities min{γ00 , γ10 , γ01 , γ11 } ≤ g(x, y) ≤max{γ00 , γ10 , γ01 , γ11 }.

4

The domain of definition for the Dij (x, y) is the square S3 in two dimensional space with eachside of length 3; i.e., S3 ≡ {(x, y) : 0 ≤ x ≤ 3; 0 ≤ y ≤ 3}. Note that for any (x, y) belonging to∑3 ∑3S3 , we have i=1 j=1 Dij (x, y) = 1. Thus the bilateral dummy variable functions Dij (x, y)will allocate any (x, y) ∈ S3 to one of the nine unit square cells that make up S3 . Denote thecell of area 1 that corresponds to x and y such that Dij (x, y) = 1 as Cij for i, j = 1, 2, 3. Thus
the 3 cells in the grid of 9 cells that correspond to y values that satisfy 0 ≤ y < 1 are C11 , C21
and C31 . The 3 cells that correspond to y values such that 1 ≤ y < 2 are C12 , C22 and C32
and the 3 cells that correspond to y values such that 2 ≤ y ≤ 3 are C13 , C23 and C33 .
Let f (x, y) be the function defined over S3 that we wish to approximate. Define the heights
γij of the function f (x, y) at the 16 vertices of the grid of unit area cells as follows:
γij ≡ f (i, j);

i = 0, 1, 2, 3; j = 0, 1, 2, 3.

(8)

Define the Colwell (1998; 91-92)[7] bilinear spline interpolating approximation g3 (x, y) tof (x, y) for any (x, y) ∈ S3 as follows:
g3 (x, y) ≡ D11 (x, y)[ϕ00 (1 − x)(1 − y) + ϕ10 (x − 0)(1 − y) + ϕ01 (1 − x)(y − 0) + ϕ11 xy]+ D21 (x, y)[ϕ10 (2 − x)(1 − y) + ϕ20 (x − 1)(1 − y) + ϕ11 (2 − x)(y − 0) + ϕ21 xy]+ D31 (x, y)[ϕ20 (3 − x)(1 − y) + ϕ30 (x − 2)(1 − y) + ϕ21 (3 − x)(y − 0) + ϕ31 xy]+ D12 (x, y)[ϕ01 (1 − x)(2 − y) + ϕ11 (x − 0)(2 − y) + ϕ02 (1 − x)(y − 1) + ϕ12 xy]+ D22 (x, y)[ϕ11 (2 − x)(2 − y) + ϕ21 (x − 1)(2 − y) + ϕ12 (2 − x)(y − 1) + ϕ22 xy]+ D32 (x, y)[ϕ21 (3 − x)(2 − y) + ϕ31 (x − 2)(2 − y) + ϕ22 (3 − x)(y − 1) + ϕ32 xy]+ D13 (x, y)[ϕ02 (1 − x)(3 − y) + ϕ12 (x − 0)(3 − y) + ϕ03 (1 − x)(y − 2) + ϕ13 xy]+ D23 (x, y)[ϕ12 (2 − x)(3 − y) + ϕ22 (x − 1)(3 − y) + ϕ13 (2 − x)(y − 2) + ϕ23 xy]+ D33 (x, y)[ϕ22 (3 − x)(3 − y) + ϕ32 (x − 2)(3 − y) + ϕ23 (3 − x)(y − 2) + ϕ33 xy]. (9)It can be verified that g3 (x, y) is a continuous function of x and y over S3 and g3 (x, y) is equal
to the underlying function f (x, y) when (x, y) is a vertex point of the grid; i.e., we have thefollowing equalities for the 16 vertex points in S3 :g3 (i, j) = γij ≡ f (i, j);

i = 0, 1, 2, 3; j = 0, 1, 2, 3.

(10)For each square of unit area in the grid, it can be seen that g3 (x, y) behaves like the bilinear
interpolating function g(x, y) that was defined by (2) in the previous section. Thus if (x, y)
belongs to the cell Cij where i and j are equal to 1, 2 or 3, then g3 (x, y) is bounded frombelow by the minimum of the 4 vertex point values γi−1,j−1 , γi,j−1 , γi−1,j , γi,j and boundedfrom above by the maximum of the 4 vertex point values γi−1,j−1 , γi,j−1 , γi−1,j , γi,j .
Following Colwell (1998; 89)[7], if we set y = j where j = 0, 1, 2 or 3, then the resulting
function of x, g3 (x, j), is a linear spline function in x between 0 and 3; i.e., g3 (x, j) is acontinuous, piecewise linear function of x that has 3 (joined) linear segments that can change
their slopes at the break points x = 1 and x = 2. Similarly, if we set x = i where i = 0, 1, 2
or 3, then the resulting function of y, g3 (i, y), is also a linear spline function in y between 0
and 3. Thus we can view g3 (x, y) as an interpolating function that merges these linear spline
functions in the x and y directions into a consistent continuous function of two variables,
where the interpolating function is equal to the function of interest at the 16 vertex points ofthe grid.
5

Following Poirier (1976; 11-12)[43] and Colwell (1998)[7], we can move from the interpolation
model defined by (9) to an econometric estimation model. Thus suppose that we can observex and y for N observations, say (xn , yn ) for n = 1, . . . , N . Suppose also that we can observef (xn , yn ) for n = 1, . . . , N . Finally, suppose that we can approximate the function f (x, y) by
g3 (x, y) over S3 . Let γ ≡ [γ00 , γ10 , . . . , γ33 ] be the vector of the 16 γij which appear in (9)and rewrite g3 (x, y) as g3 (x, y, γ). Now view γ as a vector of parameters which appear in thefollowing linear regression model:
zn = g3 (xn , yn , γ) + εn ;n = 1, . . . , N.

(11)

If we are willing to assume that the approximation errors εn are independently distributedwith 0 means and constant variances, the unknown parameters γij in (11) (which are theheights of the “true” function f (x, y) at the vertices in the grid) can be estimated by a leastsquares regression. It can be seen that this method for fitting a two dimensional surface overa bounded set is essentially a nonparametric method. If the number of observations N is
suﬃciently large and the observations are more or less uniformly distributed over the grid,then we can make the grid finer and finer and obtain ever closer approximations to the true
underlying function.*6
To see how this nonparametric approach to the estimation of a surface could be applied in thecontext of sales of land plots in a geographical area, suppose that in a particular time period,
we have information on the selling price of N land plots. Suppose that the selling price ofland plot n is Pn and the area of the property is Ln square meters. Suppose also that wehave data on the latitude and longitude of property n, Xn and Yn for n = 1, . . . , N . Translate
and scale these spatial coordinates into the variables xn and yn using definitions (4) and (5)above. We suppose that N is large enough and the observations are dispersed through all 9
cells in the 3 × 3 geographical grid. An approximation to the true land price surface in thegeographical area under consideration (which gives the price of land per meter squared as afunction of the transformed spatial coordinates) can be generated by estimating the following
linear regression model:
Pn /Ln = g3 (xn , yn , γ) + εn ;n = 1, . . . , N(12)where the g3 (xn , yn , γ) are defined by (9) for each (xn , yn ) in the sample of observations.
Thus estimates for the 16 unknown height parameters γij in equations (12) can be obtainedby solving a simple least squares minimization problem.
If observations are plentiful, then the grid can be made finer. Thus the 3 × 3 grid could be
replaced by a k × k grid where k is an arbitrary positive integer. In this case, definitions(5) are replaced by x ≡ k(X − Xmin )/(Xmax − Xmin ) and y ≡ k(Y − Ymin )/(Ymax − Ymin ).
Definitions (6) to (9) can readily be modified to define the approximating function gk (x, y, γ)
in place of g3 (x, y, γ). Of course the new parameter vector γ in gk (x, y, γ) will have dimension(k + 1)2 in place of the parameter vector γ in g3 (x, y, γ) which had dimension 42 = 16.
Thus Colwell (1998)[7] realized that the well known bilinear interpolation function g(x, y)*6 If the dependent variable is observed with random errors, then the method for fitting the surface canalso be regarded as a smoothing method. The smoothing parameter is the number of cells in the grid,k2 (or k can be used as the smoothing parameter); the smaller the number of cells, the smoother willbe the estimated gk (x, y) function. For a discussion of smoothing methods and alternative smoothing
parameters, see Buja, Hastie and Tibshirani (1989)[4].

6

defined by (2) could be used as a basic building block in a powerful nonparametric method
for approximating an arbitrary continuous function of two variables.*7
However, Colwell did not exhibit the explicit representation for g3 (x, y) defined by (9) so it is
not clear exactly how he defined his linear regression model. Colwell (1998; 92)[7] also madethe following statement about his method of parameterization: “As indicated earlier, one of
the location variables must be omitted if perfect multicollinearity is to be avoided. Finally,
it is not necessary to have data points within every section.” Thus he seemed to suggest that
one of the γij on the right hand side of (9) needed to be omitted in order to avoid perfectmulticollinearity. But such an omission would seem to destroy the flexibility of his method;i.e., setting say γij = 0 means that we would no longer have gk (i, j) = f (i, j). Moreover, as
we shall see later in our empirical application of his method, problems can arise if some cells
have no observations. Thus although the spirit of his model is clear, the exact details on how
to implement it are not spelled out in his paper.*8

4 Colwell’s Nonparametric Method versus Penalized Least Squares
It is useful to compare the nonparametric method for estimating a function of two variables
explained in the previous section with the nonparametric method used by Hill and Scholz(2018)[35] in their property price regressions for Sydney Australia. These authors used apenalized least squares approach for their nonparametric method.
Using the notation surrounding (11) above, a simplified version of this approach works asfollows: find a function g(x, y) which is a solution to the following penalized least squares
minimization problem:∑Nming n=1 [zn − g(xn , yn )]2 + λJ(g)(13)
where it is assumed that g(x, y) is twice continuously diﬀerentiable and J(g) is some functionof the second order partial derivatives of g evaluated at the N observed (xn , yn ).*9 The positive
parameter λ trades oﬀ how well each g(xn , yn ) approximates the observed zn with how variable
g is.
There is an extensive literature on solving this problem which is quite complicated.*10 In order
to illustrate some of the problems associated with this penalized least squares approach, we*7 Colwell (1998; 87)[7] summarized his method as follows: “A simple, non-parametric approach is needed—

one that fits any function with the fewest possible restrictions. The purpose of this article is to describe
a method for using a single, standard OLS regression to estimate a continuous price function in spacethat can approximate any shape. The cost of the method developed here is found in terms of degrees
of freedom. It achieves flexibility by requiring large numbers of observations.” Colwell (1998; 88)[7]
after noting that his approximating function was diﬀerentiable in the interior of each square in the gridbut not necessarily at boundary points of each square oﬀered the following view on the importanceof continuity versus diﬀerentiability: “This tradeoﬀ of continuity for diﬀerentiability is worth accepting
because continuity is compelling, whereas the worth of diﬀerentiability is dubious. Continuity of the pricefunction is important because markets produce continuity. Discontinuities are destroyed by arbitrage.”
We agree with his assessment that diﬀerentiability of the approximating surface is not essential.
*8 Poirier (1976; 59-62)[43] developed an approach which is equivalent to our Colwell based approach exceptthat his parameterization of the approximating function is in terms of changes in levels rather than in
the levels themselves. Thus his interaction terms are diﬃcult to interpret. He also did not deal with thediﬃculties associated with empty cells.
∑N*9 For example, J(g) could equal
n=1 [gxx (xn , yn ) + 2gxy (xn , yn ) + gyy (xn , yn )] where gxx (xn , yn ) ≡2∂ g(xn , yn )/∂x∂y, etc.
*10 For example, see Wahba and Wendelberger (1980)[55], Silverman (1985; 19)[50], Wahba (2000)[54] andWood (2004)[58].

7will consider a simplified one dimensional version of this approach using finite diﬀerences ofg(x) in J(g) in place of partial derivatives of g. For simplicity, we will also assume that thexn data are unique and we order the N observations on x as x1 < x2 < · · · < xN . The
corresponding observed z values are zn for n = 1, . . . , N . Again, for simplicity, we assume
that the xn are equally spaced.
Set g(xn ) = sn for n = 1, . . . , N . Our first highly simplified version problem (13) is the following penalized least squares minimization problem: choose s1 , s2 , . . . , sN to solve the following
unconstrained minimization problem:
∑N
∑N
(14)
mins1 ,...,sN { n=1 [zn − sn ]2 + λ n=3 [∆2 sn ]2 }where λ > 0 is a positive tradeoﬀ parameter and the first and second order finite diﬀerencesof the sn are defined as follows:∆sn ≡ sn − sn−1 ;n = 2, 3, . . . , N ;(15)∆2 sn ≡ ∆sn − ∆sn−1 ;n = 3, 4, . . . , N.

(16)

For a given λ, (14) can readily be solved using the first order conditions for the minimization problem and a bit of linear algebra. Denote the solution to (14) as the vectors(λ) ≡ [s1 (λ), . . . , sN (λ)]. Denote the vector of observed zn as z ≡ [z1 , . . . , zN ]. As λ tends
to 0, s(λ) will tend to the observed vector z. As λ tends to plus infinity, the sn (λ) will tend
to a linear function of n; i.e., sn (λ) will tend to α + βn for n = 1, . . . , N for some α and β.
This smoothing model was originally suggested by Henderson (1924; 30)[32]. Note that thissmoothing method depends on the choice of λ. The method of cross validation can be used
to choose λ; see Silverman (1985; 5)[50] for references to the literature.
Our second highly simplified version problem (13) is the following penalized least squares
minimization problem: choose s1 , s2 , . . . , sN to solve the following unconstrained minimization
problem:∑N∑N
mins1 ,...,sN { n=1 [zn − sn ]2 + λ n=3 [∆3 sn ]2 }(17)
where λ > 0 is again a positive tradeoﬀ parameter between fit and the variability of the sn
and the third order finite diﬀerences of the sn , are defined as follows:∆3 sn ≡ ∆2 sn − ∆2 sn−1 ;n = 4, 5, . . . , N.

(18)

Denote the solution to (17) as the vector s(λ) ≡ [s1 (λ), . . . , sN (λ)]. As λ tends to 0, s(λ) willtend to the observed vector z. As λ tends to plus infinity, the sn (λ) will tend to a quadratic
function of n; i.e., sn (λ) will tend to α + βn + γn2 for n = 1, . . . , N for some α and β. Thissmoothing model was originally suggested by Whittaker (1923)[57].
In the actuarial literature, the smoothing methods that chose the sn = g(xn ) for n = 1, . . . , N
by solving (14) or (17) for an exogenous λ is known as the Whittaker-Henderson method of
graduation*11 and in the economics literature, using (14) to smooth a time series is known as
the Hodrick-Prescott (1980)[36] filter.
This penalized least squares approach to smoothing a series implicitly defines a series to besmooth if its higher order diﬀerences are all “small”. However, Bizley (1958; 126)[2] criticized*11 In the early actuarial literature, the process of smoothing a mortality table was known as graduating thedata; i.e., the hills and valleys of the observed “rough” series, the zn , were to be graded into a smoothroad, the smoothed series, the sn . See Sprague (1887; 112)[51].

8this definition of smoothness by noting that the rather smooth exponential function, g(x) ≡ ex ,has derivatives and diﬀerences that never become small, no matter how high a diﬀerence istaken. This fairly compelling criticism of the penalized least squares approach has been largely
ignored by the current literature.
A method for smoothing a discrete series zn can be modeled as a mapping of the vectorz ≡ [z1 , . . . , zN ] into a “smoothed” vector s ≡ [s1 , . . . , sN ]. Let F (z) ≡ [F1 (z), . . . , FN (z)]be the vector valued smoothing function that transforms the “rough” z into the “smooth” sso that s ≡ F (z). The function F (z) is a representation of the smoothing method. Diewert
and Wales (2006; 107-110)[25] developed a test or axiomatic approach to describe desirable
properties of a smoothing method. We list their tests below along with two additional tests.*12∑N∑N
Test 1; Sum Preserving Test. If s = F (z), then 1N · F (z) ≡ n=1 Fn (z) = n=1 zn ≡ 1N · z
where 1N is an N dimensional vector of ones. The test says that the sum of the values of thesmoothed series should equal the sum of the values of the original series.*13∑N∑N
Test 2; First Moment Preserving Test: If s = F (z), then n=1 nsn = n=1 nzn . This test
was suggested by Whittaker (1923; 68)[57].
Test 3; Identity Test: If z = k1N where k is a scalar, then s = F (k1N ) = k1N . Thus if therough z is constant, then its smooth s reproduces this constant vector.
Test 4; The Linear Trend Test: If zn = α + βn for n = 1, . . . , N where α and β are constants,
then F (z) = z.
Test 5; The Quadratic Trend Test: If zn = α + βn + γn2 for n = 1, . . . , N where α, β and γ
are constants, then F (z) = z.
Test 6; The Cubic Trend Test: If zn = α + βn + γn2 + ϕn3 for n = 1, . . . , N where α, β, γ andϕ are constants, then F (z) = z.
The last 3 tests were listed in Diewert and Wales (2006; 106)[25]. The following two tests were
not listed in Diewert and Wales but they are obvious tests that are similar to Tests 4-6: if therough is a smooth elementary function of one variable, then the smooth should be identicalto the rough.
Test 7; The Exponential Trend Test: If zn = αen for n = 1, . . . , N where α is a constant, thenF (z) = z.
Test 8; The Logarithmic Trend Test: If zn = α ln(n) for n = 1, . . . , N where α is a constant,then F (z) = z.
∑N
∑N
Test 9; The Diminishing Variation Test: s = F (z) implies s · s ≤ z · z or n=1 s2n ≤ n=1 zn2 .
If the smoothing method satisfies Tests 1 and 9, then the variance of the smooth cannot exceed
the variance of the rough. Test 9 was proposed by Schoenberg (1946; 52)[46].
The next test is of fundamental importance in our view and it is essentially due to Sprague.
It is worth quoting him on this test:*12 Diewert and Wales allowed F (z) to be a set valued function rather than a single valued function. Sincemany smoothing methods generate a smooth that is a solution to a minimization problem; solutions tosuch problems may not be unique. For simplicity, here we assume unique solutions.
*13 This test implies that the arithmetic mean of the smoothed series equals the arithmetic mean of theoriginal series. Thus this test could also be called the mean preserving test. Sprague (1887; 79)[51]
suggested this test.

9

“I now proceed to a diﬀerent part of my subject and prove that it is undesirable toemploy such formulas as Mr. Woolhouse’s, Mr. Higham’s, or Mr. Ansell’s, not only
because, as already mentioned, they will never entirely get rid of the irregularities in ourobservations, but also because they all have a tendency to introduce an error even intoa regular series of numbers. I start with the proposition which I think will commanduniversal consent, that, if we attempt to graduate a perfectly regular series of numbers,
the result should be to leave it unaltered; and that, if our method of procedure alters
the law of the series, and substitutes for the original series one following a diﬀerent law,
this proves that our method of procedure is faulty.” T.B. Sprague (1887; 107-108)[51].
It can be seen that Tests 3-8 above are essentially due to Sprague. Diewert and Wales (2006;109)[25] argued that a consequence of the above quotation is the following test:
Test 10; The Smoothing Invariance Test: s = F (z) implies s = F (s) so that F [F (z)] = F (z).
Thus if we smooth the raw data z once and obtain the smooth s = F (z) and then if we smooththe resulting s and obtain F (s), we find that the second round of smoothing just reproduces s
so that F (s) = s. Put another way, the smoothing method defined by the function F should
produce a smooth series and so another round of smoothing should not change the smoothseries produced by the initial use of F .
It can be shown that the Henderson smoothing method defined by the solution to (14) satisfies
all of the above tests except Tests 6-8 and Test 10 and the Whittaker method defined by the solution to (17) satisfies all of the above tests except Tests 7, 8 and 10.*14 The failure of a method
to pass Test 10 is, in our view, is a serious problem with the method. Unfortunately, as notedby Diewert and Wales (2006; 109)[25], most smoothing methods fail this test. For example,of the seven main types of nonparametric smoothing models listed by Buja, Hastie and Tibshirani (1989; 456-460)[4]: (i) running mean smoothers; (ii) bin smoothers; (iii) running line
smoothers; (iv) polynomial regression; (v) cubic smoothing splines (the Henderson (1924)[32]
model); (vi) regression splines with fixed knots or break points; (vii) kernel smoothers; only
methods (ii), (iv) and (vi) pass Test 10. The reason why these three smoothing methods pass
Test 10 is that they are linear smoothers that are based on linear regression models.
Thus suppose that the rough z satisfies the linear regression model, z = Xβ + ε where
X is an N × K matrix of exogenous variables of full rank K ≤ N and ε is a vector ofindependently distributed error terms with means 0 and constant variances. Then the leastsquares estimator for β is b ≡ (XT X)−1 Xz and the predicted z vector is the smoothed vectors ≡ Xb = X(XT X)−1 Xz = Sz where S ≡ X(XT X)−1 X is the linear smoothing matrix
for this regression based smoothing method. Thus F (z) ≡ Sz for this linear smoothing
method. Any linear regression based smoothing method will satisfy Test 10 since F [F (z)] =S[Sz] = Sz = F (z) for this class of methods since SS = X(XT X)−1 X X(XT X)−1 X =X(XT X)−1 X = S. Methods (ii), (iv) and (vi) are all special cases of a linear regression basedsmoothing method.*15
What we are arguing in this section is that linear regression based smoothing methods havesome advantages over the penalized least squares approach used by Hill and Scholz in their*14 See Diewert and Wales (2006; 107-110)[25] who drew on the earlier work of Whittaker (1923; 67)[57],Greville (1944; 205-211)[30], Schonberg (1946; 52)[46] and Buja, Hastie and Tibshirani (1989; 466)[4].
is spanned by the columns of X, then the linear regression based smoothing
method will satisfy Tests 1, 3, 9 and 10. If in addition, the columns of X span the linear trend vector[1, 2, . . . , N ]T , then the linear smoother Sz = X(XT X)−1 Xz will also satisfy Tests 2 and 4. Tests 5-8
will not be satisfied unless the columns of X span the particular smooth trends that are specified in thesetests.

*15 If the vector of ones, 1N

10

study. Looking at the definition of g3 (x, y) in equation (9), it can be seen that our suggested
bivariate nonparametric smoothing method is a linear regression based smoothing method,where the γij are the components of the β vector in a linear regression. Thus our suggested
method inherits the useful properties of a linear regression model including satisfaction ofa bivariate version of Test 10; i.e., if we smooth the raw data once and then smooth once
again, we do not change the original smooth, whereas the penalized least squares approach
used by Hill and Scholz does not satisfy this key test. Also, if there are no random errors
in the model and if the observed zn occur precisely at the vertex points of the chosen k × k
grid, then the Colwell model will reproduce the underlying functional values; i.e., we will havegk (xn , yn ) = f (xn , yn ) for all n where f (xn , yn ) is the “true” function value at the nth grid
point. Thus our model has an underlying flexibility which is not attained by a penalized leastsquares approach.
We turn now to our empirical application of the Colwell nonparametric method of surface
fitting.

5 The Tokyo Residential Property Sales Data
Our basic data set consists of quarterly data on V (the selling price of a residential property
in Tokyo), L (the land area of the property in square meters), S (the floor space area of thestructure if any on the land plot), A (the age in years of the structure if any on the land plot),
the location of the property (specified in terms of longitude x and latitude y and in termsof the 23 Wards or local neighbourhoods of Tokyo) and some additional characteristics to be
explained below. These data were obtained from a weekly magazine, Shukan Jutaku Joho
(Residential Information Weekly) published by Recruit Co., Ltd., one of the largest vendorsof residential listings information in Japan. The Recruit dataset covers the 23 special wardsof Tokyo for the period 2000 to 2010, including the mini-bubble period in the middle of 2000s
and its later collapse caused by the Great Recession. Shukan Jutaku Joho provides time seriesof housing prices from the week when it is first posted until the week it is removed due toits sale.*16 We only used the price in the final week because this can be safely regarded as
suﬃciently close to the contract price.*17
After range deletions, there were a total of 5580 observations with structures on the property
in our sample of sales of residential property sales in the Tokyo area over the 44 quarters
covering 2000-2010.*18 In addition, we had 8493 observations on residential properties withno structure on the land plot.*19 Thus there was a total of 14,073 properties in our sample.
*16 There are two reasons for the listing of a unit being removed from the magazine: a successful deal or awithdrawal (i.e. the seller gives up looking for a buyer and thus withdraws the listing). We were allowed
access to information regarding which the two reasons applied for individual cases and we discarded
those transactions where the seller withdrew the listing.
*17 Recruit Co., Ltd. provided us with information on contract prices for about 24 percent of all listings.
Using this information, we were able to confirm that prices in the final week were almost always identicalwith the contract prices; see Shimizu, Nishimura and Watanabe (2012)[49].
*18 We deleted 9.2 per cent of the observations with structures because they fell outside our range limits forthe variables V, L, S, A, N B and W . It is risky to estimate hedonic regression models over wide ranges
when observations are sparse at the beginning and end of the range of each variable. The a priori range
limits for these variables were as follows: 1.8 ≤ V ≤ 20; 0.5 ≤ S ≤ 2.5; 0.5 ≤ L ≤ 2.5; 1 ≤ A ≤ 50;
2 ≤ N B ≤ 8; 25 ≤ W ≤ 90. For properties with no structure, we set the corresponding S equal to 0.
*19 The large number of plots with no structures can be explained by the preference of Japanese buyers ofresidential properties to construct their own house. Thus sellers of residential properties that have arelatively old structure on the property tend to demolish the structure and sell the property as a land
only property.

11

The variables used in our regression analysis to follow and their units of measurement are as
follows:
V
S
L
A
NB
W
TW
TT
X
Y
PS

= The value of the sale of the house in 10,000,000 Yen;
= Structure area (floor space area) in units of 100 meters squared;= Lot area in units of 100 meters squared;= Approximate age of the structure in years;= Number of bedrooms;= Width of the lot in 1/10 meters;= Walking time in minutes to the nearest subway station;= Subway running time in minutes to the Tokyo station from the nearest station
during the day (not early morning or night);= Longitude of the property;= Latitude of the property;= Construction cost for a new structure in 100,000 Yen per meter squared.

In addition, we have the address of each property and so we can allocate each property to oneof the 23 Wards of Tokyo. This information was used to construct Ward dummy variables foreach property in our sample. The basic descriptive statistics for the above variables are listed
in Table 1 below.
Table 1 Descriptive Statistics for the Variables
Name
V
S
L
A
NB
W
TW
TT
X
Y
PS

No. of Obs.
14073
14073
14073
14073
14073
14073
14073
14073
14073
14073
14073

Mean
6.24910.434641.03885.82311.566946.8289.382931.244139.6735.6781.7733

Std. Dev
2.9016
0.58280.39869.117
2.0412
12.5414.31557.3882
0.06340.05590.0294

Min.
1.8
0
0.5
0
0
25
1
8
139.5635.543
1.73

Max.
20
2.47892.497749.7238902948
139.9235.816
1.85

Thus over the sample period, the sample average sale price was approximately 62.5 million
Yen, the average structure space was 43.5 m2 (but for properties with structures, the average
was 110 m2 ), the average lot size was 103.9 m2 , the average age of the structure was 5.8
years (for properties with a structure, the average age was 14.7 years), the average numberof bedrooms in the properties that had structures was 3.95, the average lot width was 4.7
meters, the average walking time to the nearest subway station was 9.4 minutes and theaverage subway travelling time from the nearest station to the Tokyo Central station was 31.2
minutes.
As is usual in property regressions using L and S as independent variables, we can expect12multicollinearity problems in a simple linear regression of V on S and L.*20
In order to eliminate a possible multicollinearity problem between the lot size L and floor
space area S for properties with a structure and to make our estimates of structure value
consistent with structure value estimates in the Japanese national accounts, we will assume
that the value of a new structure in any quarter is equal to a Residential Construction Cost
Index per m2 for Tokyo*21 (equal to PSt for quarter t) times the floor space area S of thestructure.

6 The Basic Builder’s Model using Spatial Coordinates to Model
Land Prices
The builder’s model for valuing a residential property postulates that the value of a residential
property is the sum of two components: the value of the land which the structure sits on plus
the value of the residential structure.
In order to justify the model, consider a property developer who builds a structure on aparticular property. The total cost of the property after the structure is completed will beequal to the floor space area of the structure, say S square meters, times the building cost
per square meter, β say, plus the cost of the land, which will be equal to the cost per square
meter, α say, times the area of the land site, L. Now think of a sample of properties of thesame general type, which have prices or values Vtn in period t*22 and structure areas Stnand land areas Ltn for n = 1, . . . , N (t) where N (t) is the number of observations in period t.
Assume that these prices are equal to the sum of the land and structure costs plus error terms
εtn which we assume are independently normally distributed with zero means and constantvariances. This leads to the following hedonic regression model for period t where the αt andβt are the parameters to be estimated in the regression:*23
Vtn = αt Ltn + βt Stn + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t).

(19)

Note that the two characteristics in our simple model are the quantities of land Ltn and thequantities of structure floor space Stn associated with property n in period t and the twoconstant quality prices in period t are the price of a square meter of land αt and the price ofa square meter of structure floor space βt . Finally, note that separate linear regressions canbe run of the form (19) for each period t in our sample.
The hedonic regression model defined by (19) applies to new structures. But it is likely thata model that is similar to (19) applies to older structures as well. Older structures will be*20 See Diewert (2010)[14] and Diewert, de Haan and Hendriks (2011)[17] (2015)[18] for evidence on thismulticollinearity problem using Dutch data.
*21 This index was constructed by the Construction Price Research Association which is now an independentagency but prior to 2012 was part of the Ministry of Land, Infrastructure, Transport and Tourism(MLIT), a ministry of the Government of Japan. The quarterly values were constructed from theMonthly Residential Construction Cost index for Tokyo.
*22 The period index t runs from 1 to 44 where period 1 corresponds to Q1 of 2000 and period 44 corresponds
to Q4 of 2010.
*23 Other papers that have suggested hedonic regression models that lead to additive decompositions ofproperty values into land and structure components include Clapp (1980)[6], Francke and Vos (2004)[28],Gyourko and Saiz (2004)[31], Bostic, Longhofer and Redfearn (2007)[3], Davis and Heathcote (2007)[9],Francke (2008)[27], Koev and Santos Silva (2008)[39], Statistics Portugal (2009)[52], Diewert (2008)[13](2010)[14], Rambaldi, McAllister, Collins and Fletcher (2010)[44] and Diewert, Haan and Hendriks(2011)[17] (2015)[18].

13worth less than newer structures due to the depreciation of the structure. Assuming that wehave information on the age of the structure n at time t, say Atn = A(t, n) and assuming ageometric depreciation model, a more realistic hedonic regression model than that defined by(19) above is the following basic builder’s model :*24
Vtn = αt Ltn + βt (1 − δ)A(t,n) Stn + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t)(20)

where the parameter δ reflects the net depreciation rate as the structure ages one additional
period.*25 Note that (20) is now a nonlinear regression model whereas (19) was a simple linear
regression model.
Note that the above model is a supply side model as opposed to the demand side model of
Muth (1971)[42] and McMillen (2003)[40]. Basically, we are assuming competitive suppliers of
housing so that we are in Rosen’s (1974; 44)[45] Case (a), where the hedonic surface identifies
the structure of supply. This assumption is justified for the case of newly built houses but it
is less well justified for sales of existing homes.*26
As was mentioned in the previous section, we have 14,073 observations on sales of houses inTokyo over the 44 quarters in years 2000-2010. Thus equations (20) above could be combined
into one big regression and a single depreciation rate δ could be estimated along with 44 land
prices αt and 44 new structure prices βt so that 89 parameters would have to be estimated.
However, experience has shown that it is usually not possible to estimate sensible land andstructure prices in a hedonic regression like that defined by (20) due to the multicollinearity
between lot size and structure size.*27 Thus in order to deal with the multicollinearity problem,
we draw on exogenous information on new house building costs from the Japanese Ministryof Land, Infrastructure, Transport and Tourism (MLIT). Thus if the sale of property n inperiod t has a new structure on it, we assume that the value of this new structure is equal tothis measure of residential building costs pSt time the floor space area of the new structure,
Stn . We apply this same line of reasoning to property sales that have old structures on themas well. Thus our new builder’s model replaces the parameter βt which appears in equations(20) with the exogenous oﬃcial price PSt . Our new model becomes the following one:
Vtn = αt Ltn + PSt (1 − δ)A(t,n) Stn + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t).

(21)

*24 This formulation follows that of Diewert (2008)[13] (2010)[14], Diewert, Haan and Hendriks (2011)[17](2015)[18], de Haan and Diewert (2013)[10] and Diewert and Shimizu (2015a)[20]. It is a special case of
Clapp’s (1980; 258)[6] hedonic regression model. For applications of the builder’s model to condominium
sales, see Diewert and Shimizu (2017a)[22] and Burnett-Issacs, Huang and Diewert (2016)[5].
*25 This estimate of depreciation is regarded as a net depreciation rate because it is equal to a “true”
gross structure depreciation rate less an average renovations appreciation rate. Since we do not haveinformation on renovations and additions to a structure, our age variable will only pick up average grossdepreciation less average real renovation expenditures. Note that we excluded sales of houses from oursample if the age of the structure exceeded 50 years when sold. Very old houses tend to have larger thannormal renovation expenditures and thus their inclusion can bias the estimates of the net depreciation
rate for younger structures.
*26 Thorsnes (1997; 101)[53] assumed that a related supply side model held instead of equation (20). He
assumed that housing was produced by a CES production function H(L, K) ≡ [αLρ + βK ρ ]1/ρ where
K is structure quantity and ρ ̸= 0; α > 0; β > 0 and α + β = 1. He assumed that property value Vntt ) where pt , ρ, α and β are parameters to be estimated. However, our builder’sis equal to pt H(Ltn , Kn
model assumes that the production functions that produce structure space and that produce land are
independent of each other.
*27 See Schwann (1998)[47], Diewert (2010)[14] and Diewert, de Haan and Hendriks (2011)[17] (2015)[18] on
the multicollinearity problem.

14

Thus we have 14,073 degrees of freedom to estimate 44 land price parameters αt and one
annual geometric depreciation rate parameter δ, a total of 45 parameters. We estimatedthe nonlinear regression model defined by (21) for our Tokyo data set using the econometricprogramming package Shazam; see White (2004)[56]. The R2 for the resulting preliminary
nonlinear regression Model 0 was only 0.5545,*28 which is not very satisfactory. However,there are no location variables in Model 0.
The value of a structure of the same type and age should not vary much from location tolocation. However, the price of land will definitely depend on the location of the property.
Thus for our next model, we assume that the per meter price of land of a property is a function
f (x, y) of its spatial coordinates, x and y. Thus let xtn and ytn equal the normalized longitude
and latitude of property n sold in period t. We will initially approximate the true land pricesurface f (x, y) by the 4 × 4 Colwell spatial grid function g4 (x, y) defined above in section 3.
If Xtn and Ytn are the raw longitude and latitude of property n sold in period t, then define
the corresponding transformed spatial coordinates as xtn ≡ 4(Xtn − Xmin )/(Xmax − Xmin )and ytn ≡ 4(Ytn − Ymin )/(Ymax − Ymin ) and define the Colwell approximation to f (xtn , ytn )as g4 (xtn , ytn ) using the definitions in section 3. Model 1 is the following nonlinear regression
model:
Vtn = αt g4 (xtn , ytn , γ)Ltn + PSt (1 − δ)A(t,n) Stn + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t). (22)Note that the γ vector of parameters in g4 (xtn , ytn , γ) consists of the 25 spatial grid parameters
γij where i, j = 0, 1, 2, 3, 4. Thus equations (22) contain 44 unknown period t land priceparameters αt , 25 unknown γij spatial grid parameters and 1 depreciation rate parameter δ
for a total of 70 unknown parameters. However, not all of these parameters can be estimated.
If we multiply all components of γ by the positive number λ and divide all αt by λ, it canbe verified that the terms αt g4 (xtn , ytn , γ)Ltn remain unchanged. Thus a normalization on
the αt and the γij is required. We impose the normalization α1 = 1 which means that thesequence, 1, α2 , . . . , α44 , can be interpreted as an index of residential land prices for Tokyo for
the 44 quarters in our sample, where the index is set equal to 1 in the first quarter of 2000.*29
There are 4 × 4 = 16 cells Cij in our grid of squares where C11 is the cell in the southwest
corner of the grid, C41 is the southeast corner cell, C14 is the northwest corner cell and C44
is the northeast corner cell. It turns out that cell C41 has no observed property sales overthe entire sample period.*30 This means that γ44 , the value of land per meter squared at
the southeast corner of the grid, cannot be identified. Thus in addition to the normalization
α = 1, we set γ44 = 0 in equations (22). These normalizations will ensure that the nonlinear*28 All of the R2 reported in this paper are equal to the square of the correlation coeﬃcient between thedependent variable in the regression and the corresponding predicted variable. The estimated net annual
geometric depreciation rate was δ = 10.49%, with a T statistic of 23.3. This depreciation rate is too highto be believable. As we add more explanatory variables, we will obtain more reasonable depreciation
rates.
*29 Note that the αt shift the entire land price surface g4 (x, y, γ) in a proportional manner over time. Thus
all reasonable index numbers of the land price components of individual residential properties in Tokyo
will be proportional to the estimated parameter sequence 1, α∗2 , . . . , α∗44 . This is perhaps a weaknessof our model but given the nonparametric nature of our modeling of land prices, some simplifying
assumptions had to be made in order to estimate all of the parameters in our model. In a real time
setting, a rolling window approach would be used in order to implement our model which would allow theheight parameters to change over time; see Shimizu, Nishimura and Watanabe (2010)[48] for an exampleof this approach.
*30 This cell is defined as properties with normalized spatial coordinates (x, y) where x and y satisfy the
restrictions 3 ≤ x ≤ 4 and 0 ≤ y ≤ 1.

15

minimization problem associated with estimating Model 1 will have a unique solution. Thus
Model 2 has 68 unknown parameters.
We used Shazam’s nonlinear regression option to estimate the unknown parameters in (22).
The R2 for Model 1 turned out to be 0.7973, a huge jump from the R2 for Model 0, which
was only 0.5545. This large jump indicates the importance of including locational variables
in a property regression. The log likelihood for Model 1 increased by 5524.50 points over the
final log likelihood of Model 0 for adding 23 new location parameters. Since Model 0 is a
special case of Model 1, this is a highly significant increase in log likelihood. The estimatedgeometric depreciation rate from Model 1 was 6.33% per year (T statistic = 31.7) which ismore reasonable than the Model 0 estimate of 10.49%.
We now address the problem of how exactly should the land, structure and overall house
price index be constructed? Our nonlinear regression model defined by (22) decomposes
the period t value of property n into two terms: one which involves the land area Ltn ofthe property, αt g4 (xtn , ytn , γ)Ltn , and another term, PSt (1 − δ)A(t,n) Stn , which involves the
structure area Stn of the property. The first term can be regarded as an estimate of theland value of house n that was sold in quarter t while the second term is an estimate of thestructure value of the house (if Stn > 0). Our problem now is how exactly should these twovalue terms be decomposed into constant quality price and quantity components? Our viewis that a suitable constant quality land price index for all houses sold in period t should be
αt and for property n sold in period t, the corresponding constant quality quantity should be
g4 (xtn , ytn , γ)Ltn .*31 Turning to the decomposition of the structure value of property n soldin period t, PSt (1 − δ)A(t,n) Stn , into price and quantity components, we take PSt as the priceand (1 − δ)A(t,n) Stn as the corresponding quantity for property n sold in quarter t.
Note that the above value decompositions of individual property values into land and structurecomponents sets the price of a square meter of land in quarter t equal to αt∗ , the estimatedparameter value for αt and sets the price of a square meter of structure equal to PSt , theoﬃcial per meter structure cost for quarter t. These prices are assumed to be the same acrossall properties sold in period t and thus we can set the aggregate land and structure price for all
residential properties sold in period t equal to PLt and PSt where PLt ≡ αt∗ for t = 1, . . . , 44.
The corresponding aggregate constant quality quantities of land and structures sold in periodt are defined as follows:
QLt ≡∑N (t)

n=1 g4 (xtn , ytn , γ∗)Ltn ; QSt ≡∑N (t)

n=1 (1 − δ∗ A(t,n))Stn ;t = 1, . . . , 44

(23)

∗
∗
where γ ∗ ≡ [γ00, . . . , γ44] and δ ∗ are the estimated parameter values obtained by running thenonlinear regression model defined by (22).*32*31 An alternative way of viewing our land model is that land in each location indexed by the spatial

coordinates xn , yn can be regarded as a distinct commodity with its own price and quantity. But since
our model forces all land prices in the same location to move proportionally over time, virtually all index
number formulae will generate an overall land price series that is proportional to the αt .
*32 We could use hedonic imputation or index number theory to form aggregate price and quantity indexesof land and structures but because our model makes the constant quality price of land and structures thesame across all property sales in a quarter, our aggregation procedure can be viewed as an application of
Hicks’ Aggregation Theorem; i.e., if the prices in a group of commodities vary in strict proportion overtime, then the factor of proportionality can be taken as the price of the group and the deflated groupexpenditures will obey the usual properties of a microeconomic commodity. “Thus we have demonstrated
mathematically the very important principle, used extensively in the text, that if the prices of a group of
goods change in the same proportion, that group of goods behaves just as if it were a single commodity.”
J.R. Hicks (1946; 312-313)[33].

16The price and quantity series for land and structures need to be aggregated into an overall
Tokyo residential property sales price index. We use the Fisher (1922)[26] ideal index toperform this aggregation. Thus define the overall house price level for quarter t for Model 2, Pt ,as the chained Fisher price index of the land and structure series {PLt , PSt , QLt , QSt }. Since
these aggregate price and quantity series are generated by the Model 1 nonlinear regression
model defined by equations (22), we relabel QLt , QSt , Pt , PLt , PSt , as QL1t , QS1t , Pt1 , PL1t , PS1t
for t = 1, . . . , T.*33
1.3
1.2
1.1
1
0.9
0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
PMean t
𝑷𝑷𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌

𝑷𝑷P1t
𝟏𝟏𝟏𝟏

PL1t 𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋 pSt

𝑷𝑷𝐒𝐒𝐒𝐒

Figure 1 Mean Property Price Index and Model 1 Overall and Land Price Indexes andthe Oﬃcial Structure Price Index

The overall Model 1 house price index P1t as well as the land and structure price indexes PL1t
and the normalized structure price index, pSt ≡ PSt /PS1 , for Tokyo over the 44 quarters in
the years 2000-2010 are graphed in Figure 1.*34 We have also computed the quarterly mean
selling price of properties traded in quarter t and then normalized this average property priceseries to start at 1 in Quarter 1 of 2000. This mean price series, PMean t , is also graphed in
Figure 1.*35
It can be seen that the oﬃcial structure price series gradually trends downward over the sample
period, which is not surprising since general deflation occurred in Japan during our sample
period. Because there are so many land only properties in our sample and since the value ofstructures is relatively small for properties which have structures on them, it can be seen that
our estimated land price series, PL1t , is relatively close to our Model 1 overall property priceindex, P1t . It can also be seen that the average property price series, PMean t , has the same
For t = 1, define P1t ≡ 1. For t > 1, define P1t
in terms of P1t−1 and PFt as P1t ≡ P1t−1 PFt where PFt is the quarter t Fisher chain link index. The
chain link index for t ≥ 2 is defined as PFt ≡ [PLASt PPAAt ]1/2 where the Laspeyres and Paasche chain
link indexes are defined as PLASt ≡ [PL1t QL1t−1 + PS1t QS1t−1 ]/[PL1t−1 QL1t−1 + PS1t−1 QS1t−1 ] andPPAAt ≡ [PL1t QL1t + PS1t QS1t ]/[PL1t−1 QL1t + PS1t−1 QS1t ]. Diewert (1976)[11] (1992)[12] showedthat the Fisher formula had good justifications from both the perspectives of the economic and axiomatic
approaches to index number theory.
*34 Define the normalized oﬃcial structure price series as pSt = PSt /PS1 for t = 1, . . . , 44. This is the series
that is plotted in Figure 1. It will not change as we introduce additional hedonic property regression
models. We note that the oﬃcial index PSt = 18.5pSt ; i.e., PS1 = 18.5.
*35 The series PMean , P1 , PL1 and pS are also listed in Table A1 of the Appendix.

*33 The Fisher chained index P1t is defined as follows.

17general shape as our overall property price index P1t , but the average property price series lies
well below our constant quality property price series by the end of the sample period. This is
to be expected since the mean property price series does not take into account depreciationof the structures for properties that have structures on them. However, the extent of thedownward bias in the mean property price series by the end of the sample period is somewhat
surprising.
Can we vary the number of cells in the spatial grid and explain more of the variation inresidential property prices? We address this question in the next four hedonic regression
models (Models 2-5) where we progressively increase the number of cells in the locational
grid. Thus we will replace the land price approximating function g4 (xtn , ytn , γ) in (22) byg5 (xtn , ytn , γ), g6 (xtn , ytn , γ), g7 (xtn , ytn , γ) and g8 (xtn , ytn , γ). The resulting Models 2-5 have25, 36, 49 and 64 cells Cij and 36, 49, 64 and 81 spatial land price height parameters γij
respectively. Setting up the corresponding nonlinear regressions using (22) as a template isstraightforward except that the existence of cells with no sample observations means that not
all height parameters can be estimated.
For Model 2, which used g5 (xtn , ytn , γ) in (22) in place of g4 (xtn , ytn , γ), the following cells in
the 5 × 5 grid of cells had no sales over our sample period: C11 , C41 , C51 and C42 . This meansthat 3 height parameters could not be estimated so we imposed the following restrictions on
the parameters of Model 2: γ00 = γ40 = γ50 = 0. We also set α1 = 1 so that the remaining
land price parameters αt could be identified. Thus Model 2 had 36 − 3 = 33 γij parameters,
43 land price parameters αt and 1 depreciation rate parameter δ for a total of 77 parameters.
The final log likelihood for Model 2 was 155.04 points higher than the final log likelihood forModel 1 for adding 9 extra land price location parameters. The resulting R2 was 0.8035 andthe estimated geometric depreciation rate was δ ∗ = 6.29% with a T statistic of 31.6. We∗
expected that all of the estimated height parameters would be positive but two of them (γ51∗and γ05 ) turned out to be negative. However, the estimated land prices for each observation tn
in our sample, g5 (xtn , ytn , γ ∗ ), turned out to be positive for t = 1, . . . , 44 and n = 1, . . . , N (t),∗and so we did not worry about these 3 negative γij
at this stage of our investigation.*36 The∗
sequence of estimated αt is our estimated land price series for Model 2, PL2t , and this seriesis plotted in Figure 2 below and is listed in Table A2 of the Appendix.
For Model 3, which used g6 (xtn , ytn , γ) in (22) in place of g4 (xtn , ytn , γ), the following 5 cells
in the 6 × 6 grid of cells had no sales over our sample period: C11 , C51 , C61 , C52 and C62 . Thus
we set the following 5 height parameters equal to 0 in order to identify the remaining heightparameters: γ00 = γ50 = γ60 = γ51 = γ61 = 0. We also set α1 = 1 so that the remaining land
price parameters αt could be identified. Thus Model 3 had 49 − 5 = 44 γij parameters, 43 land
price parameters αt and 1 depreciation rate parameter δ for a total of 88 parameters. The
final log likelihood for Model 3 was 82.43 points lower than the final log likelihood for Model
2 for adding 11 extra land price location parameters. Model 3 is not a special case of Model 2
so it can happen that a moving to a larger number of squares in the grid does not improve thefit of the model. The problem is that there are likely to be discrete neighbourhood land priceeﬀects and our relatively course partition of the city into squares does not adequately capture*36 The city of Tokyo is adjacent to the Pacific Ocean and so the boundaries of the city do not fit nicely

into a rectangular grid (which we transformed into a square grid). Thus as the number of squares in thegrid becomes larger, some squares at the boundaries of the grid will end up having no observations orvery few observations. Thus suppose the observations in cell C11 are concentrated in the top north east
corner of this cell. Then a better fit to the observed data in cell C11 may be obtained by setting γ00
equal to a negative number.

18

these discrete neighbourhood eﬀects. The resulting R2 for Model 3 was 0.8014 (less than the
Model 2 R2 of 0.8035) and the estimated geometric depreciation rate was δ ∗ = 6.25% witha T statistic of 31.8. There were 5 negative estimates for the land price height parameters:
∗
∗
∗
∗
∗
γ01
, γ10
, γ41
, γ06
and γ51. However, the estimated land prices g6 (xtn , ytn , γ ∗ ) turned out to bepositive for each observation in our sample. The sequence of estimated αt∗ is our estimatedland price series for Model 3, PL3t , and this series is plotted in Figure 2 below and is listed inTable A2 of the Appendix.
Model 4 used g7 (xtn , ytn , γ) in (22) in place of g4 (xtn , ytn , γ). The following 9 cells in the 7 × 7
grid of cells had no sales over our sample period: C11 , C21 , C51 , C61 , C71 , C52 , C62 , C72 and C17 .
Thus we set the following 9 height parameters equal to 0 in order to identify the remaining
height parameters: γ00 = γ10 = γ50 = γ60 = γ70 = γ51 = γ61 = γ71 = γ07 = 0. We also set
α1 = 1 so that the remaining land price parameters αt could be identified. Thus Model 4 had
64 − 9 = 55 γij parameters, 43 land price parameters αt and 1 depreciation rate parameterδ for a total of 99 parameters. The final log likelihood for Model 4 was 501.88 points higher
than the final log likelihood for Model 3 for adding 11 extra land price location parameters.
The resulting R2 for Model 4 was 0.8156 and the estimated geometric depreciation rate wasδ ∗ = 5.99% with a T statistic of 31.9. There were 3 negative estimates for the land price
∗
∗
∗
, γ67
and γ77. As usual, the estimated land prices, g7 (xtn , ytn , γ ∗ ) forheight parameters: γ01
t = 1, . . . , T and n = 1, . . . , N (t), turned out to be positive for each observation in our sample.
The sequence of estimated αt∗ is our estimated land price series for Model 4, PL4t , and thisseries is plotted in Figure 2 below and is listed in Table A2 of the Appendix.
Finally, Model 5 used g8 (xtn , ytn , γ) in (22) in place of g4 (xtn , ytn , γ). The following 14 cells in
the 8×8 grid of cells had no sales over our sample period: C11 , C12 , C21 , C18 , C61 , C62 , C63 , C71 ,C72 , C73 , C81 , C82 , C83 and C88 . All 4 corner cells were empty along with many other boundary
cells. Thus we set the following 14 height parameters equal to 0 in order to identify theremaining height parameters: γ00 = γ10 = γ01 = γ60 = γ61 = γ62 = γ70 = γ71 = γ72 = γ80 =
γ81 = γ82 = γ88 = 0. We also set α1 = 1 so that the remaining land price parameters αt could
be identified. Thus Model 5 had 91 − 14 = 77 γij parameters, 43 land price parameters αt
and 1 depreciation rate parameter δ for a total of 111 parameters. The final log likelihood forModel 5 was 249.72 points lower than the final log likelihood for Model 4 for adding 12 extra
land price location parameters. The resulting R2 for Model 5 was 0.8086 (compared to 0.8156
for Model 4) and the estimated geometric depreciation rate was δ ∗ = 6.18% with a T statistic∗∗∗∗of 31.5. There were 5 negative estimates for the land price height parameters: γ02
, γ11
, γ50
, γ51
∗
∗
and γ52 . As usual, the estimated land prices, the g8 (xtn , ytn , γ ), turned out to be positivefor each observation in our sample. The sequence of estimated αt∗ is our estimated land priceseries of index numbers for Model 5, PL5t , and this series is plotted in Figure 2 below and islisted in Table A2 of the Appendix.
At this point, we decided stop the process of increasing the number of height parameters. It
is clear that our best model up to this point was Model 4.
One of the main purposes of this paper is to see if the use of spatial coordinates in a residential
hedonic property value regression can lead to more accurate estimates for a property priceindex and for a land price subindex for residential properties than can be obtained using justpostal codes or other neighbourhood locational variables. Hill and Scholz (2018)[35] made thiscomparison for residential property price indexes but not for the land price component of theiroverall property price index since their methodological approach did not allow for separate
land and structure subindexes.
An alternative to using spatial coordinates to measure the influence of location on property
19prices is to use postal codes or neighbourhoods as indicators of location. There are 23 Wards
in Tokyo and each property in our sample belongs to one of these Wards. In order to take
into account possible neighbourhood eﬀects on the price of land, we introduced ward dummy
variables, DW,tn,j , into the hedonic regression (20). These 23 dummy variables are defined asfollows: for t = 1, . . . , 44; n = 1, . . . , N (t); j = 1, . . . , 23:*37{
DW,tn,j ≡1 if observation n in period t is in Ward j of Tokyo;
0 if observation n in period t is not in Ward j of Tokyo.

(24)

We now modify the model defined by (20) to allow the level of land prices to diﬀer across the
23 Wards of Tokyo. The new Model 6 is defined by the following nonlinear regression model:∑23
Vtn = αt ( j=1 ωj DW,tn,j )Ltn +PSt (1−δ)A(t,n) Stn +εtn ; t = 1, . . . , 44; n = 1, . . . , N (t). (25)Comparing the models defined by equations (20) and (25), it can be seen that we have added
an additional 23 ward relative land value parameters, ω1 , . . . , ω23 , to the model defined by (20).
However, looking at (25), it can be seen that the 44 land price time parameters (the αt ) andthe 23 ward parameters (the ωj ) cannot all be identified. Thus we need to impose at least one
identifying normalization on these parameters. We chose the following normalization α1 = 1.
Thus equations (25) contain 43 unknown period t land price parameters αt , 23 Ward relative
land price parameters, the ωj , which replace the 25 unknown γij spatial grid parametersin (22), and 1 depreciation rate parameter δ for a total of 67 unknown parameters. Thus
this Ward dummy variable hedonic regression (Model 6) has roughly the same number ofparameters as our spatial coordinate Model 1, which had 68 unknown parameters.
The final log likelihood for Model 6 was −24318.67, a gain of 5045.90 over the final log
likelihood of Model 0 defined by equations (21). The R2 for Model 6 was 0.7853. The final log
likelihood for Model 1 was −23840.07 and the R2 was 0.7993. Thus the spatial coordinates
Model 1 fit the data better than the dummy variable Model 6. Both models had roughly
the same number of parameters. However, how diﬀerent are the resulting land price indexes
generated by these two models? As usual, the sequence of estimated αt∗ is our estimated land
price series for Model 6, PL6t , and this series is plotted in Figure 2 below and is listed in Table
A2 of the Appendix.*38
It can be seen that all 6 models produce much the same land price indexes.*39 Since our best
fitting model was Model 4, PL4t is our preferred land price series. Note that the Ward dummy
variable model land price index, PL6t , is fairly close to our preferred series.
The above 6 models make use of information on land plot size, structure floor space, theage of the structure (if the property has a structure) and its location, either in terms of*37 The number of observations in each Ward in our sample was as follows: 3, 5, 195, 429, 348, 28, 62, 94,453, 1260, 1114, 3434, 382, 701, 2121, 274, 107, 76, 432, 1679, 361, 212, 303. Thus Wards 1 and 2 hadvery few observations.
*38 Define the aggregate constant quality amounts of residential land and structures sold in period t by
∑N (t) ∑∑N (t)∗∗ A(t,n) S
QLt ≡ n=1 ( 23tn for t = 1, . . . , 44. The overall
n=1 (1 − δ )j=1 ωj DW,tn,j )Ltn and QSt ≡period t property price index for Model 6, P6t , is defined as the chained Fisher price index using theabove QLt and QSt as the period t quantity series and PL6t ≡ α∗t and the oﬃcial structure prices PSt
as the period t price series when constructing the Fisher index chain links.
*39 Since the structure component of overall property prices is relatively small compared to the land component and since the structure price index is the same across all 6 models, the overall property priceindexes generated by Models 1-6 are all very similar.

20

1.3
1.2
1.1
1
0.9
0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋 PL1t

PL2t
𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋

PL3t
𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋

PL4t𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋

PL5t

𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋 PL6t

𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋

Figure 2 Land Prices for Models 1-6

spatial coordinates or terms of its neighbourhood.*40 These are the most important residential
property price determining characteristics in our view. In the following section, we make use ofadditional information on housing characteristics and see if this extra information materiallychanges our estimated land price indexes.*41 We will use the spatial coordinate Model 4 as
our starting point in the models which follow, since it was the best fitting model studied inthis section. This model used the Colwell nonparametric model for modeling the land pricesurface with the 7 × 7 = 49 cell grid.

7 Spatial Coordinate Models that Use Additional Information
It is likely that property sales that have an older structure on the property will have a diﬀerent
land valuation than a nearby property of the same size that consists of cleared land, sincedemolition costs are not trivial. Our Model 7 takes this possibility into account. Define thedummy variable DL,tn as follows for t = 1, . . . , 44 and n = 1, . . . , N (t):{DL,tn ≡

1 if observation n in period t is a land only sale;
0 otherwise.

(26)

Define DS,tn ≡ 1 − DL,tn for t = 1, . . . , 44; n = 1, . . . , N (t). Thus if property n sold in period t
has a structure on it, DS,tn will equal 1. Model 7 estimates the following nonlinear regression:
Vtn = αt (DS,tn + ϕDL,tn )g7 (xtn , ytn , γ)Ltn + PSt (1 − δ)A(t,n) Stn + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t).

(27)

Thus the parameter ϕ gives the added premium to the property’s land price (per metersquared) if the property has no structure on it. We expect ϕ to be a small number. Since we are*40 We also require an exogenous building cost per square meter in order to get realistic land and structure

subindexes.
*41 We are also interested in determining whether the extra information will change our estimates of structure

depreciation rates.

21

using the interpolation function g7 (xtn , ytn , γ) as a basic building block in the nonlinear regression model defined by (27), we need to impose the same restrictions on the γij that were imposed in Model 4. Thus we set the following 9 height parameters equal to 0 in order to identify
the remaining height parameters: γ00 = γ10 = γ50 = γ60 = γ70 = γ51 = γ61 = γ71 = γ07 = 0.
We also set α1 = 1 so that the remaining land price parameters αt could be identified. Thus
Model 7 had 64 − 9 = 55 γij parameters, 43 land price parameters αt , 1 depreciation rate
parameter δ and one land only premium parameter ϕ for a total of 100 parameters. As starting coeﬃcient values for Model 7, we used the final coeﬃcient estimates from Model 4 plus
the starting value ϕ = 0. The final log likelihood for Model 7 was 128.75 points higher than
the final log likelihood for Model 4 for adding 1 land only parameter. The resulting R2 forModel 7 was 0.8175 (the Model 4 R2 was 0.8156) and the estimated geometric depreciation
rate was δ ∗ = 2.85% with a T statistic of 15.4. Recall that the estimated depreciation ratefrom Model 4 was 5.99%. Our new estimated depreciation rate is much more reasonable. The
estimated ϕ was ϕ∗ = 1.110 (T statistic = 153.4). Thus a property without a structure sold at
an 11.0% premium compared to a similar property without a structure. There were 3 negative∗∗∗
estimates for the land price height parameters: γ01, γ67
and γ77. As usual, the estimated land∗
prices, g7 (xtn , ytn , γ ) for t = 1, . . . , T and n = 1, . . . , N (t), turned out to be positive for eachobservation in our sample. The sequence of estimated αt∗ is our land price series for Model 7,
PL7t , and this series is plotted in Figure 3 below and is listed in Table A3 of the Appendix.
The most important point we learned from running this regression model is that residential
property sales in Japan with and without a structure on the property are qualitatively different. Taking this diﬀerence into account led to much more reasonable estimated structure
depreciation rates.
In our next model, we allow the per square meter price of land to vary as the size of the land plotincreases. Recall that we have restricted the range of the land variable to 0.5 ≤ Ltn ≤ 2.5.*42
We allow the price of land to be piecewise linear function of the plot size with 3 break points;
1, 1.5 and 2. Using these land area break points, we found that 7492 observations fell into
the interval 0.5 ≤ Ltn < 1, 4711 observations fell into the interval 1 ≤ Ltn < 1.5, 1414
observations fell into the interval 1.5 ≤ Ltn < 2 and 456 observations fell into the interval
2 ≤ Ltn ≤ 2.5. We label the four sets of observations that fall into the above four groups
as groups 1-4. For each observation n in period t, we define the four land dummy variables,
DL,tn,k , for k = 1, 2, 3, 4 as follows:*43{
DL,tn,k ≡1 if observation tn has land area that belongs to group k;
0 if observation tn has land area that does not belong to group k.

(28)

These dummy variables are used in the definition of the following piecewise linear function of
Ltn , fL (Ltn ), defined as follows:fL (Ltn , λ) ≡ DL,tn,1 [λ0 L0 + λ1 (Ltn − L0 )] + DL,tn,2 [λ0 L1 + λ1 (L1 − L0 ) + λ2 (Ltn − L1 )]+ DL,tn,3 [λ0 L0 + λ1 (L1 − L0 ) + λ2 (L2 − L1 ) + λ3 (Ltn − L2 )]+ DL,tn,4 [λ0 L0 + λ1 (L1 − L0 ) + λ2 (L2 − L1 ) + λ3 (L3 − L2 ) + λ4 (Ltn − L3 )](29)*42 Recall that our units of measurement for land are in 100 meters squared so that Ltn = 1 means thatobservation n in period t had a land area equal to 100 m2 .
*43 Note that for each observation, the land dummy variables sum to one; i.e., for each tn, DL,tn,1 +DL,tn,2 +DL,tn,3 + DL,tn,4 = 1.

22

where λ ≡ [λ0 , λ1 , λ2 , λ3 , λ4 ] and the λk are 5 unknown parameters and L0 ≡ 0.5, L1 ≡ 1, L2 ≡1.5 and L3 ≡ 2. The function fL (Ltn , λ) defines a relative valuation function for the land areaof a house as a function of the plot area. Thus λ0 can be interpreted as the marginal priceof land for plots between 0 and 0.5, λ1 can be interpreted as the marginal price of land forplots between 0.5 and 1, λ2 can be interpreted as the marginal price of land for plots between
1 and 1.5 and so on.
Model 8 is the following nonlinear regression:
Vtn = αt (DS,tn + ϕDL,tn )g7 (xtn , ytn , γ)fL (Ltn , λ) + PSt (1 − δ)A(t,n) Stn + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t)(30)

where the function fL is defined above by (29) and εtn is an error term. There are 44 unknown
land price parameters αt , 1 land only premium parameter ϕ, 64 land price height parameters
γij , 5 marginal price of land parameters λk and 1 depreciation rate δ to estimate. However, as
was the case with Model 4 and the previous Model 7, some cells in our grid of cells are empty
and so we cannot estimate 9 of the γij and so there are only 55 γij parameters to estimate.
Also, we cannot identify all of the αt and λk so we impose the normalizations α1 = 1 andλ1 = 1. Thus we are left with 104 unknown parameters to estimate.
As starting coeﬃcient values for Model 8, we used the final coeﬃcient estimates from Model
7 plus the starting values λ0 = λ2 = λ3 = λ4 = 1. The final log likelihood for Model
8 was 328.27 points higher than the final log likelihood for Model 7 for adding 4 lot size
parameters. The resulting R2 for Model 8 was 0.8222 (the Model 7 R2 was 0.8175) andthe estimated geometric depreciation rate was δ ∗ = 3.44% with a T statistic of 16.1. The
estimated ϕ was ϕ∗ = 1.091 (T statistic = 155.9). Thus a property without a structure sold at
an 9.1% premium compared to a similar property without a structure. There were 3 negative∗∗∗
estimates for the land price height parameters: γ01, γ67
and γ77. As usual, the estimated land∗
prices, g7 (xtn , ytn , γ ) for t = 1, . . . , T and n = 1, . . . , N (t), turned out to be positive for eachobservation in our sample. The sequence of relative marginal valuations of land (the λ∗k ) wereas follows (with the T statistics in brackets): λ∗0 = 1.50(41.2), λ∗1 = 1 (imposed restriction),λ∗2 = 1.16(35.1), λ∗3 = 1.23(35.9) and λ∗4 = 0.89(13.9). Thus as lot size increases, the per meter
price of land eventually decreases. The sequence of estimated αt∗ is our index of land prices
for Model 8, PL8t , and this series is plotted in Figure 3 below and is listed in Table A3 of the
Appendix.
In our next model, we allow the per square meter price of a square meter of structure to varyas the floor space of the structure increases. The rational for this model is that bigger houses
are likely to be of higher quality. Recall that we have restricted the range of the structure floor
space variable to 0.5 ≤ Stn ≤ 2.5.*44 We allow the price of a square meter of floor space area
to be piecewise linear function of the overall floor space size with 2 break points; 1 and 1.5.
Using these structure area break points, we found that 2768 observations fell into the interval
0.5 ≤ Stn < 1, 2020 observations fell into the interval 1 ≤ Stn < 1.5 and 792 observations fell
into the interval 1.5 ≤ Stn ≤ 2.5. We label the 3 sets of observations that fall into the above
3 groups as groups 1-3. For each observation n in period t, we define the 3 structure dummy*44 Recall that our units of measurement for floor space are in 100 meters squared so that Stn = 1 meansthat observation n in period t had floor space area equal to 100 m2 .

23

variables, DS,tn,m , for m = 1, 2, 3 as follows:*45{

if observation tn has structure area that belongs to group m;
if observation tn has structure area that does not belong to group m.
(31)
These dummy variables are used in the definition of the following piecewise linear function of
Stn , fS (Stn ), defined as follows:
DS,tn,m ≡1
0

fS (Stn , µ) ≡ DS,tn,1 [µ0 S0 + µ1 (Stn − S0 )] + DS,tn,2 [µ0 S1 + µ1 (S1 − S0 ) + µ2 (Stn − S1 )]+ DS,tn,3 [µ0 S0 + µ1 (S1 − S0 ) + µ2 (S2 − S1 ) + µ3 (Stn − S2 )](32)

where µ ≡ [µ0 , µ1 , µ2 , µ3 ] and the µm are 4 unknown parameters and S0 ≡ 0.5, S1 ≡ 1 andS2 ≡ 1.5. The function fS (S, µ) defines a relative valuation function for the floor space areaof a house as a function of the total floor space area, S. Thus µ0 can be interpreted as themarginal price of a structure for structures with total floor between 0 and 0.5, µ1 can beinterpreted as the marginal price of an additional square meter of structure for total structureareas between 0.5 and 1, µ2 can be interpreted as the marginal price of an additional square
meter of structure for total structure areas between 1 and 1.5 and µ3 can be interpreted as
the marginal price of an additional square meter of structure for total structure areas between
1.5 and 2.5.
Model 9 is the following nonlinear regression:
Vtn = αt (DS,tn + ϕDL,tn )g7 (xtn , ytn , γ)fL (Ltn , λ) + PSt (1 − δ)A(t,n) fS (Stn , µ) + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t)(33)

where the function fL is defined above by (29), the function fS is defined by (32) and εtn is anerror term. There are 44 unknown land price parameters αt , 1 land only premium parameter
ϕ, 64 land price height parameters γij , 5 marginal price of land parameters λk , 4 marginal
price of structure parameters µm and 1 depreciation rate δ to estimate. However, as was thecase with the previous Model 8, some cells in our grid of cells are empty and so we cannot
estimate 9 of the γij and so there are only 55 γij parameters to estimate. Also, we cannot
identify all of the αt and λk so we impose the normalizations α1 = 1 and λ1 = 1. We also
impose the normalization µ1 = 1 in order to use the oﬃcial structure building cost index
to value new buildings with total floor space area between 0.5 and 1. This will ensure that
our estimated structure values for new buildings are close to estimated structure values based
solely on the oﬃcial cost index. Thus we are left with 107 unknown parameters to estimate.
As starting coeﬃcient values for Model 9, we used the final coeﬃcient estimates from Model
8 plus the starting values µ0 = µ2 = µ3 = 1. The final log likelihood for Model 9 was 136.32
points higher than the final log likelihood for Model 8 for adding 3 structure size parameters.
The resulting R2 for Model 9 was 0.8256 (the Model 8 R2 was 0.8222) and the estimatedgeometric depreciation rate was δ ∗ = 4.35% with a T statistic of 18.6. The estimated ϕ was
ϕ∗ = 1.159(96.8). Thus a property without a structure sold at an 15.9% premium compared
to a similar property without a structure. As usual, there were 3 negative estimates for the∗∗∗land price height parameters: γ01, γ67
and γ77. Also as usual, the estimated land prices,*45 Note that for each observation tn where Stn > 0, the structure dummy variables sum to one; i.e., foreach such tn, DS,tn,1 + DS,tn,2 + DS,tn,3 = 1. There were 5580 observations which had a positive Stn .
Note also that fS (Stn , µ) = 0 if Stn = 0.

24

g7 (xtn , ytn , γ ∗ ) for t = 1, . . . , T and n = 1, . . . , N (t), turned out to be positive for eachobservation in our sample. The sequence of relative marginal valuations of land (the λ∗k ) wereas follows: λ∗0 = 1.509(41.7), λ∗1 = 1 (imposed restriction), λ∗2 = 1.160(35.7), λ∗3 = 1.238(36.8)
and λ∗4 = 0.898(14.3). The sequence of relative marginal valuations of floor space area (theµ∗m ) were as follows: µ∗0 = 1.461(21.8), µ∗1 = 1 (imposed restriction), µ∗2 = 2.331(18.6) andµ∗3 = 1.572(10.0). The sequence of estimated αt∗ is our index of land prices for Model 9, PL9t ,and this series is plotted in Figure 3 below and is listed in Table A3 of the Appendix.
Note that the 3 models that we introduced in this section did not require any additional
property characteristics: we simply made better use of the information on S and L. However,for our next model, we make use of the two subway variables: T W , the walking time inminutes to the nearest subway station, and T T , the subway running time in minutes to theTokyo central station. The sample minimum time for T W was 1 minute and the minimum
time for T T was 8 minutes. Our next model allows the price of land to decrease as these two
subway time variables increase. These variables have proven to be highly significant in otherstudies of Tokyo property prices.*46 Thus Model 10 is the following nonlinear regression:
Vtn = αt (DS,tn + ϕDL,tn )g7 (xtn , ytn , γ)fL (Ltn , λ)[1 + τ (T Wtn − 1)][1 + ρ(T Ttn − 8)]+ PSt (1 − δ)A(t,n) fS (Stn , µ) + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t)(34)

where the function fL is defined above by (29), the function fS is defined by (32), τ is thepercentage change in the price of land due to a one minute increase in walking time, ρ is thepercentage change in the price of land due to a one minute increase in subway running time toTokyo central station and εtn is an error term. There are 44 unknown land price parameters
αt , 1 land only premium parameter ϕ, 64 land price height parameters γij , 5 marginal price ofland parameters λk , 4 marginal price of structure parameters µm , 2 subway time parametersand 1 depreciation rate δ to estimate. However, as was the case with the previous models inthis section, some cells in our grid of 49 cells are empty and so we cannot estimate 9 of the γij
and so there are only 55 γij parameters to estimate. Also, we cannot identify all of the αt andλk so we impose the normalizations α1 = 1 and λ1 = 1. We also impose the normalizationµ1 = 1. Thus we are left with 109 unknown parameters to estimate.
As starting coeﬃcient values for Model 10, we used the final coeﬃcient estimates from Model 9
plus the starting values τ = 0 and ρ = 0. The final log likelihood for Model 10 was 531.13 points
higher than the final log likelihood for Model 9 for adding 2 subway time parameters. The
resulting R2 for Model 10 was 0.8383 (the Model 9 R2 was 0.8256) and the estimated geometricdepreciation rate was δ ∗ = 4.52%(21.7). The estimated ϕ was ϕ∗ = 1.137(104.1). Thus aproperty without a structure sold at an 13.7% premium compared to a similar property withouta structure. As usual, there were 3 negative estimates for the land price height parameters:
∗
∗
∗
γ01
, γ67
and γ77. Also as usual, the estimated land prices, g7 (xtn , ytn , γ ∗ ) for t = 1, . . . , T andn = 1, . . . , N (t), turned out to be positive for each observation in our sample. The sequence ofrelative marginal valuations of land (the λ∗k ) were as follows: λ∗0 = 1.481(42.1), λ∗1 = 1 (imposed
restriction), λ∗2 = 1.137(35.1), λ∗3 = 1.216(33.5) and λ∗4 = 0.930(13.3). The sequence of relative
marginal valuations of floor space area (the µ∗m ) were as follows: µ∗0 = 1.418(23.0), µ∗1 = 1(imposed restriction), µ∗2 = 2.255(19.3) and µ∗3 = 1.540(10.2). Thus the estimated λ∗k andµ∗m did not change significantly from the estimates for Model 9. The estimated subway timeparameter were τ ∗ = −0.0123(33.3) and ρ∗ = −0.00606(19.1). Thus a 1 minute increase in*46 See for example Shimizu, Nishimura and Watanabe (2010)[48] and Diewert and Shimizu (2015a)[20](2017a)[22].

25walking time to the nearest subway station decreases the per square meter land price by 1.2%.
A 1 minute time in commuting time to the Tokyo central station decreases land value by 0.6%.
The sequence of estimated αt∗ is our land price index for Model 10, PL10t , and this series isplotted in Figure 3 below and is listed in Table A3 of the Appendix.
In our next model, we introduce the number of bedrooms N Btn as a property characteristicthat can aﬀect structure value if the property n in quarter t has a structure on it. For theproperties in our sample, the number of bedrooms ranged from 2 to 8.*47 Since there wererelatively few observations with 6, 7 or 8 bedrooms, we grouped these last 3 categories into asingle category. Define the bedroom dummy variables DN B,tn,i for observation tn as follows
for i = 2, 3, 4, 5; t = 1, . . . , 44 and n = 1, . . . , N (t):{DN B,tn,i ≡1 if observation tn has a structure on it with i bedrooms;
0 elsewhere.

(35)

For bedroom group 6, define DN B,tn,6 ≡ 1 if observation tn has a structure on it with 6, 7 or
8 bedrooms and define DN B,tn,6 ≡ 0 elsewhere.
Model 11 is the following nonlinear regression:
Vtn = αt [DS,tn + ϕDL,tn ]g7 (xtn , ytn , γ)fL (Ltn , λ)[1 + τ (T Wtn − 1)][1 + ρ(T Ttn − 8)]∑6+ PSt (1 − δ)A(t,n) fS (Stn , µ)[ i=2 κi DN B,tn,i ] + εtn ; t = 1, . . . , 44; n = 1, . . . , N (t) (36)where the all of the functions and parameters which appear in (36) were defined in the previous
model except that we have now added 5 bedroom variables, κ2 , κ3 , κ4 , κ5 and κ6 . We makethe same normalizations as we made in Model 10 and in addition, we set κ2 = 1. Thus we haveadded 4 additional unknown κi parameters to Model 10 so Model 11 has a total 113 unknown
parameters. One might expect the κi parameters to monotonically increase as i increases;
i.e., more bedrooms indicates a higher quality structure. But we have already introduced theµm into our hedonic regression model which allows structure quality to increase as the floor
space increases. The correlation between the number of bedrooms and the structure area is
0.93500 and thus there will be a multicollinearity problem in using both of these variables in
our nonlinear regression. Thus we cannot expect the κi and µm to increase monotonically asi and m increase.
As starting coeﬃcient values for Model 11, we used the final coeﬃcient estimates from Model
10 plus the starting values κi = 1 for i = 3, 4, 5, 6.The final log likelihood for Model 11
was 75.03 points higher than the final log likelihood for Model 10 for adding 4 number ofbedroom parameters. The resulting R2 for Model 11 was 0.8400 (the Model 10 R2 was
0.8383). As usual, there were 3 negative estimates for the land price height parameters:
∗
∗
∗
γ01
, γ67
and γ77. Also as usual, the estimated land prices, g7 (xtn , ytn , γ ∗ ) for t = 1, . . . , T
and n = 1, . . . , N (t), turned out to be positive for each observation in our sample. Recall
that κ2 was set equal to 1. The remaining bedroom parameter estimates were as follows:κ∗2 = 1 (imposed restriction), κ∗3 = 1.1587(20.6), κ∗4 = 1.0863(21.4), κ∗5 = 0.9116(20.0) andκ∗6 = 0.7701(18.6). Evidently, a house with more bedrooms did not seem to increase thequality of the structure. The sequence of relative marginal valuations of floor space area (theµ∗m ) were as follows: µ∗0 = 1.1573(11.1), µ1∗ = 1 (imposed restriction), µ∗2 = 2.4201(14.2) andµ∗3 = 1.7643(10.7). Thus, for the most part, houses with more floor space were of higher*47 For the 5580 properties in our sample that had a structure, the number of observations that had 2,

3,. . . ,8 bedrooms was: 247, 1628, 2441, 841, 295, 90 and 38.

26

quality. The collinearity of the number of bedrooms with the floor space of the structure
explains the counterintuitive results for the κ∗i . The remaining parameters for Model 11 weremuch the same as the Model 10 estimated parameters. As usual, the sequence of estimatedαt∗ is our land price series for Model 11, PL11t , and this series is plotted in Figure 3 below andis listed in Table A3 of the Appendix.
The final additional variable that we introduced into our property nonlinear regression model
was the width of the land plot, Wtn for property sale n in period t. Recall that Wtn ismeasured in 10ths of a meter and the range of this property width variable was 25 to 90.
Other residential property hedonic regression models have shown that this variable is a verysignificant one: the greater is the lot width, the more valuable is the land plot. Model 12 is
the following nonlinear regression:
Vtn = αt [DS,tn + ϕDL,tn ]g7 (xtn , ytn , γ)fL (Ltn , λ)× [1 + τ (T Wtn − 1)][1 + ρ(T Ttn − 8)][1 + σ(Wtn − 25)]
∑6
+ PSt (1 − δ)A(t,n) fS (Stn , µ)[ i=2 κi DN B,tn,i ] + εtn ; t = 1, . . . , 44; n = 1, . . . , N (t) (37)where the all of the functions and parameters which appear in (37) were defined in the previous
model except that we have now added the property width parameter σ. We make the same
normalizations as we made in Model 11. Thus we have added 1 additional unknown parameterto Model 11 so Model 12 has a total 114 unknown parameters. Our expectation is that σ will
be positive.
As starting coeﬃcient values for Model 12, we used the final coeﬃcient estimates from Model
11 plus the starting value σ = 0. The final log likelihood for Model 12 was 401.54 points higher
than the final log likelihood for Model 11 for adding 1 lot width parameter. The resulting
R2 for Model 12 was 0.8488 (the Model 11 R2 was 0.8400). The estimate for the lot widthparameter was σ ∗ = 0.00402(27.4). Thus an extra meter of lot width adds about 4% to theper meter squared price of the land plot. As usual, the estimated land price height parameters
∗
∗
∗
∗
γ01
, γ67
and γ77
turned out to be negative. However, γ52also turned out to be negative.
∗
But, as usual, the estimated land prices, g7 (xtn , ytn , γ ) for t = 1, . . . , T and n = 1, . . . , N (t),turned out to be positive for each observation in our sample. The estimated parameters forthis model are listed in the Appendix; see Table A4. As usual, the sequence of estimated αt∗
is our land price series for Model 12, PL12t , and this series is plotted on Figure 3 is also listed
in Table A3 of the Appendix.
∗
Although the fact that Model 12 generated 4 negative estimated γij
did not lead to any
negative predicted prices for land for the properties in our sample, these negative estimates
could lead to negative land prices for properties not in our sample. Hence, it may be usefulto perform a final regression where we restrict the γij to be nonnegative.*48 This can be doneby replacing γ01 , γ67 , γ77 and γ52 in the function g7 (xtn , ytn , γ) by squares of parameters andthen rerunning the model defined by (37). Model 13 is the resulting model. The final log
likelihood for Model 13 was 1.19 points lower than the final log likelihood for Model 12 asa result of imposing nonnegativity on the height parameters γ01 , γ67 , γ77 and γ52 by entering*48 Nonparametric methods for the estimation of functions of one variable tend to become unreliable forobservations close to the boundaries of the domain of definition of the independent variable because
the nonparametric method will tend to fit the error terms near the end points of the sample range; seeDiewert and Wales (2006; 118)[25] and the literature cited there. The same problem carries over tothe nonparametric estimation of surfaces. Forcing the γij to be nonnegative will tend to mitigate thisproblem.

27these parameters as squares in the function g7 (xtn , ytn , γ). The R2 for Model 13 was 0.8488,
which is the same as the R2 for Model 12. Thus imposing nonnegativity constraints on theγij did not lead to a significant loss of fit. The estimated parameters for this model are listed
in the Appendix; see Table A5. As usual, the sequence of estimated αt∗ is our land price series
for Model 13, PL13t , and this series is listed in Table A3 of the Appendix. It does not appear
on Figure 3 because the PL13t are virtually identical to the PL12t .
Our final model in this section is a Ward dummy variable model that adds more explanatory property characteristics to Model 6 in the previous section. This model was defined byequations (25). Model 14 is defined by the following nonlinear regression model:∑23
Vtn = αt [DS,tn + ϕDL,tn ][ j=1 ωj DW,tn,j ]fL (Ltn , λ)× [1 + τ (T Wtn − 1)][1 + ρ(T Ttn − 8)][1 + σ(Wtn − 25)]
∑6
+ PSt (1 − δ)A(t,n) fS (Stn , µ)[ i=2 κi DN B,tn,i ] + εtn ; t = 1, . . . , 44; n = 1, . . . , N (t).
(38)
Thus Model∑23 14 is basically the same as Model 12 and 13 except that the Ward dummy variable
terms, j=1 ωj DW,tn,j , replace the Colwell locational grid function, g7 (xtn , ytn , γ), for eachobservation tn. There are 82 parameters in this model. The final log likelihood for Model
14 was −22492.99, a gain of 478.6 over the final log likelihood of Model 6. However, the log
likelihood of Model 14, the Ward dummy variable model with extra characteristics, was 827.00
points below the final log likelihood of Model 13, our “best” Colwell spatial coordinate model.
The R2 for Model 14 was 0.8300 which is not that far below the R2 for Model 13, which was
0.8488. The estimated parameters for Model 14 are listed in Table A6 in the Appendix.*49
The sequence of land price indexes is the series of estimated coeﬃcients, the αt∗ . This series
is labeled as PL14t and is listed in Table A3 and plotted in Figure 3 below.
∗
Excluding the location parameters (the γij
for Model 13 and the ωj∗ for Model 14), it can beseen that the remaining parameters (ϕ∗ , the λk∗ , τ ∗ , ρ∗ , σ ∗ , the µ∗m , δ ∗ and the κ∗i ) are roughly
similar across Models 13 and 14 and the land price coeﬃcients (the αt∗ ) are very close to eachother. Our tentative conclusion at this point is that neighbourhood dummy variable models do
not fit the data quite as well as a spatial coordinate model but the two types of model generatemuch the same land prices and hence overall residential property price indexes.*50 Looking at
Figure 3, it can be seen that Model 14, the model that used Ward dummy variables to take
into account location eﬀects on the price of land, produced the lowest measure of residential
land price inflation in Tokyo. Our best spatial coordinate models, Models 12 and 13,*51 had
the next lowest measure of land price inflation. The land price indexes generated by Models
7-11 are marginally above the Model 13 and 14 indexes.

In the following section, we compute the overall residential property price indexes that are
generated by Models 7-14 and we compare the resulting indexes with a traditional log pricetime dummy property price index.

*49 The estimated parameter values for ϕ, τ, ρ, σ and δ for Models 13 and 14 were 1.125, −0.0130, −0.0668,

0.00402, 0.0417 and 1.155, −0.0136, −0.00945, 0.004393, 0.0402 respectively. Thus the estimates werebroadly similar for our best spatial coordinates model and our best Ward dummy variable model.
*50 Hill and Scholz (2018)[35] came to the same conclusion for Sydney overall residential property priceindexes.
*51 We did not plot the land price index for Model 13 since it could not be distinguished from the Model 12index.

28

1.3
1.2
1.1
1
0.9
0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
PL7t

𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋

PL8t

𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋

PL9t

𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋

PL10t

𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋

PL11t

PL12t

𝑷𝑷𝐋𝐋𝐋𝐋𝐋𝐋𝐋𝐋

PL14t

Figure 3 Land Price Indexes for Models 7-12 and 14

8 Overall Residential Property Price Indexes
Models 7-14 in the previous section all have the same general structure in that property value
is decomposed into the sum of land value plus structure value plus an error term. For example,
using Model 8, the predicted value of property n in quarter t, Vtn , is equal to the predicted land
value, αt∗ (DS,tn + ϕ∗ DL,tn )g7 (xtn , ytn , γ ∗ )fL (Ltn , λ∗ ) ≡ VLtn , plus predicted structure value,∑N (t)PSt (1 − δ ∗ )A(t,n) Stn ≡ VStn . Thus quarter t total predicted land value is VLt ≡ n=1 VLtn
∑N (t)and quarter t total predicted structure value is VSt ≡ n=1 VStn . The period t price of landfor Models 7-14, PLt , is always αt∗ and the corresponding period t price of a structure is always
pSt ≡ PSt /PS1 for t = 1, . . . , 44 where PSt is the oﬃcial structure cost per m2 of structure.
For all models, define the corresponding period t aggregate quantity of land and structure as
QLt ≡ VLt /PLt and QSt ≡ VSt /pSt for t = 1, . . . , 44. Thus the basic price and quantity data
for each model are (PLt , pSt , QLt , QSt ) for t = 1, . . . , 44. The overall property price indexes for
Models 7-14 are calculated as Fisher (1922)[26] chained indexes using the price and quantity
data for land and structures that has just been defined. Label the resulting overall property
price indexes for quarter t as P7t , P8t , P9t , P10t , P11t , P12t , P13t , and P14t . These series are listed
in Table A7 in the Appendix. As was the case with the corresponding land price indexes, there
these overall property price indexes approximate each other fairly closely.
There is one additional overall property price index that we calculate in this section and thatis an index that is based on a “traditional” hedonic property price regression that uses thelogarithm of price as the dependent variable and has time dummy variables.*52 Define the kthtime dummy variable DT,tn,k for property n sold in period t as follows: for t = 1, . . . , 44; n =1, . . . , N (t); k = 2, 3, . . . , 44:
DT,tn,k ≡ 1 if t = k; DT,tn,k ≡ 0 if t ̸= k.

(39)*52 This type of model does not generate reasonable separate land and structure subindexes; see Diewert,

Huang and Burnett-Isaacs (2017; 24-25)[19] for an explanation of this assertion.

29

Our best time dummy variable hedonic regression model*53 is the following Model 15 :∑44
∑23
ln Vtn = k=2 αk DT,tn,k + j=1 ωj DW,tn,j + λ ln Ltn + µStn + δAtn + τ T Wtn + ρT Ttn∑6+ σWtn + i=3 κi DN B,tn,i + εtn ;t = 1, . . . , 44; n = 1, . . . , N (t)(40)
where ln Vtn and ln Ltn denote the natural logarithms of property value Vtn and property lot
size Ltn respectively, the DT,tn,k are time dummy variables, the DW,tn,j are Ward dummy
variables, Stn is the floor space area of the property (if there was no structure on the property
n in period t, Stn ≡ 0), T Wtn and T Ttn are the subway time variables, Wtn is the lot widthvariable, Atn is the age of the structure on property n sold in period t (Atn ≡ 0 if the property
had no structure) and the DN B,tn,i are the bedroom dummy variables. The log likelihoodof this model cannot be compared to the log likelihood of the previous models because thedependent variable is now the logarithm of the property price instead of the property price.
There are 75 unknown parameters in the model defined by equations (40). The R2 for Model∗. The
15 was 0.8323. Set α1∗ = 0 and denote the estimated α2 to α44 by α2∗ , α3∗ , . . . , α44
sequence of overall property price indexes P15t generated by this model are the exponentialsof the αt∗ ; i.e., define P15t ≡ exp[αt∗ ] for t = 1, . . . , 44. This series is also listed in Table A7 of
the Appendix.
Figure 4 below compares several of the overall residential property prices that are defined
above: the mean property price index PMean t that appeared in Figure 1 above, P9t (thisis based on Model 9 which did not use information on the subway variables, the number ofbedrooms and the lot width variable), Model 13 (P13t : our best Colwell spatial coordinatesmodel), Model 14 (P14t : our best Ward dummy variable model) and Model 15 (P15t : our best
traditional log price time dummy hedonic regression model that used all of our property pricecharacteristics except the spatial coordinates).
1.3
1.2
1.1
1
0.9
0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
PMean t
𝑷𝑷𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌
𝐭𝐭

𝑷𝑷𝟗𝟗𝟗𝟗P9t

P13t
𝑷𝑷𝟏𝟏𝟏𝟏𝟏𝟏

P14t𝑷𝑷𝟏𝟏𝟏𝟏𝟏𝟏

P15t

𝑷𝑷𝟏𝟏𝟏𝟏𝟏𝟏

Figure 4 Selected Overall Property Price Indexes

Several points emerge from a study of Figure 4:*53 We ran an initial linear regression using Ltn as an independent variable in place of ln Ltn .

However,
this regression had a log likelihood which was 204.99 points lower than our final linear regression definedby (40). The R2 for this preliminary regression was 0.8274. Note that we could not use ln Stn as anindependent variable because many observations had no structure on them and hence Stn is equal to 0
for these properties and thus we could not take the logarithm of 0.

30• The mean index, PMean t , has a large downward bias as compared to the other 4 indexes
which is due to its neglect of structure depreciation. However, the movements in thisindex are similar to the movements in the other indexes.
• The property price index P15t generated by a traditional log price time dummy hedonic
regression model has a downward bias but it is not large.*54• The Model 9 property price index, a Colwell spatial coordinates model that used only
the 4 fundamental characteristics of a residential property (land plot area, structure
floor space area, the age of the structure and some locational variable)*55 generated an
overall property price index P9t that is quite close to our best Colwell spatial model,
Model 14 which generated the overall property price index P14t . Thus it is probably
not necessary for national statistical agencies to collect a great deal of information on
housing characteristics in order to produce a decent overall property price index (as well
as decent land and structure subindexes).
• The Model 14 property price index, P14t , that used local neighbourhood information
about properties instead of spatial coordinate information turned out to be fairly close
to our best Colwell spatial index, P13t . Thus following the advice of Hill and Scholz(2018)[35], it is probably not necessary to utilize spatial coordinate information in order
to construct a satisfactory overall residential property price index.

9 Conclusion
Here are the main points that emerge from our paper:• Satisfactory residential land price indexes and overall residential property price indexes
can be constructed using local neighbourhood dummy variables as explanatory variables
in residential property regression models. It is not necessary to use spatial coordinates
to model location eﬀects on property prices.
• However, the use of spatial coordinates to model location eﬀects does lead to better
fitting regression models.
• The most important housing characteristics information that is needed in order to construct satisfactory residential land and overall property price indexes is information on
lot size, floor space area of the property structure (if there is a structure on the property), the age of the structure and some information on the location of the property. In
order to obtain a satisfactory land price index, our method requires the use of exogenous
information on residential construction costs.
• However, additional information on the characteristics of the property will improve
the fit of our hedonic regressions but the eﬀects of the additional information on the
resulting land and structure price indexes was minimal for our application to Tokyo
residential property price indexes.
• Having land only sales of residential properties should help improve the accuracy of theland price index that is generated by a property regression model. However, for ourJapanese data, we found that the value of the land component of a land only property
earned a 10-15% premium over the land value of a neighbouring property of the same
size but with a structure on the property. We attribute this premium to the costs ofdemolishing an older structure.
*54 Diewert (2010)[14] also observed a similar result.
*55 In addition to these four fundamental variables, we need an exogenous building cost measure in order toimplement our basic models.

31• Our models that used spatial coordinates to account for locational eﬀects on the valueof land used Colwell’s nonparametric method for fitting a surface. This nonparametricmethod is much easier to implement than the penalized least squares approach used byHill and Scholz (2018)[35] to model locational eﬀects on property prices. In section 4of the paper, we pointed out some of the theoretical advantages of Colwell’s method.
• The potential bias in using property price indexes that are based on taking mean ormedian averages of property prices in a period can be very large. Typically, thesemethods will have a downward bias due to their neglect of structure depreciation.
• A traditional log price time dummy hedonic regression model that has structure age
as an explanatory variable will typically reduce the bias that is inherent in an index
based on taking averages of property prices. For our Tokyo data, we found that thetraditional hedonic regression model led to an index which had a small downward bias;
see Figure 4 in the previous section.
It should be noted that if a national statistical agency were to apply the regression models
that were explained in this paper, they would not just run a regression using the entire sample
data. A rolling window approach would be used: a window length of say 12 to 16 quarters
would be chosen and as the data for each new quarter was processed, the movements in theindex over the last two quarters in the sample would be used to update the last publishedindex value; see Shimizu, Nishimura and Watanabe (2010)[48] for an application of this rolling
window approach.*56
Our emphasis in this paper (and in other papers*57 ) has been to develop reliable methods
for the construction of the land component of residential property price indexes. This task isimportant for national statistical agencies because the Balance Sheet Accounts in the System
of National Accounts requires estimates for the price and volume of land used in production
and consumption. In particular, this information is required in order to obtain more accurate
estimates of national (and sectoral) Total Factor Productivity growth*58 but for the vast
majority of countries, this information is simply not available. We hope that the methods
explained in the present paper will be of use to national statistical agencies in developing
improved estimates for the price and volume of land used in production and consumption.

A Supplementary Tables*56 See Diewert and Fox (2017)[15] for a discussion of other possible methods that could be used to link theindexes generated by successive windows.
*57 See Diewert and Shimizu (2015a)[20] (2015b)[21] (2017a)[22] (2017b)[23] (2019)[24] and Diewert, Fox

and Shimizu (2016)[16].
*58 See Jorgenson and Griliches (1967)[37] (1972)[38] who developed the methodology used by national andinternational statistical agencies to measure TFP growth or Multifactor Productivity growth.

32

Table A1: Mean Property Price Index and Model 1 Overall Property Price Index withLand and Structure Subindexes
t
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

PMean t1.00000
1.04097
1.05097
1.01849
1.02284
0.98708
0.97987
0.95594
0.962130.925940.950960.99168
0.96137
0.94352
0.97532
0.954700.97323
0.948210.90469
0.94306
0.93779
0.99237
1.00223
1.01111
1.053101.07737
1.12105
1.05456
1.12150
1.127641.07651
1.08274
0.93306
0.95904
0.92830
0.856650.82004
0.852410.88506
0.79792
0.83303
0.835940.850400.86796P1t
1.00000
0.98918
1.02235
0.99183
0.98759
0.972450.96833
0.916180.96526
0.92538
0.93889
0.97632
0.97547
0.97167
0.93979
0.99830
0.982190.98386
0.95997
1.025481.04702
1.06959
1.06468
1.09460
1.09683
1.11087
1.18040
1.19449
1.219031.26251
1.213291.25004
1.19154
1.144131.151541.04925
0.99146
1.01351
1.00130
0.97515
1.01996
1.06940
1.04038
1.0666533

PL1t
1.00000
0.98918
1.024960.991480.98779
0.971200.967160.91246
0.96492
0.922280.93699
0.976440.97625
0.97257
0.93788
1.00062
0.983440.98561
0.95983
1.02965
1.05240
1.075971.07080
1.10251
1.10519
1.11907
1.19230
1.207161.23350
1.27970
1.22695
1.26592
1.20469
1.15314
1.159821.050140.98891
1.012100.997930.97133
1.018361.070881.03972
1.06734PSt
1.00000
0.989190.989190.99459
0.98378
0.989190.98378
0.97297
0.962160.96757
0.962160.96757
0.95676
0.95135
0.95676
0.95135
0.95135
0.94595
0.95135
0.940540.940540.94595
0.94595
0.940540.935140.95135
0.95135
0.95135
0.94595
0.94054
0.95676
0.956760.94054
0.95135
0.96757
0.96216
0.95135
0.95676
0.97297
0.95676
0.95676
0.95135
0.962160.96216

Table A2: Land Price Indexes for Models 1-6
t
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

PL1t
1.00000
0.98918
1.024960.991480.98779
0.971200.967160.91246
0.96492
0.922280.93699
0.976440.97625
0.97257
0.93788
1.00062
0.983440.98561
0.95983
1.02965
1.05240
1.075971.07080
1.10251
1.10519
1.11907
1.19230
1.207161.23350
1.27970
1.22695
1.26592
1.20469
1.15314
1.159821.050140.98891
1.012100.997930.97133
1.018361.070881.03972
1.06734

PL2t
1.00000
0.995621.024310.997020.99087
0.980530.97469
0.915780.960900.91627
0.92883
0.975410.972510.96853
0.93576
1.005700.99016
0.993440.96747
1.02773
1.05913
1.07920
1.07246
1.10792
1.11619
1.13244
1.19785
1.204341.23573
1.28027
1.23436
1.26387
1.210421.15323
1.155481.05138
0.98995
1.00258
1.00305
0.969641.01128
1.072411.03946
1.06518

PL3t
1.00000
0.99083
1.022410.99306
0.98804
0.97689
0.97183
0.91658
0.963280.91697
0.93167
0.97671
0.974350.97079
0.932481.005400.98658
0.98815
0.963311.02756
1.05877
1.07865
1.07464
1.10687
1.10947
1.12064
1.19483
1.19851
1.22889
1.28135
1.229131.26272
1.21491
1.15710
1.15607
1.05357
0.99088
1.00928
1.00200
0.973131.017121.07038
1.04408
1.0634134

PL4t
1.00000
1.00571
1.031450.99861
1.00480
0.988200.980430.926120.971220.93082
0.93960
0.97791
0.976140.97334
0.93682
1.005310.995120.99427
0.97820
1.035131.07369
1.07899
1.08646
1.11603
1.12826
1.13779
1.209301.22033
1.24735
1.29594
1.23777
1.288121.22545
1.17238
1.16891
1.059761.00107
1.01280
1.013160.984841.030691.080171.05292
1.07786

PL5t
1.00000
0.99755
1.03206
0.999210.995460.980810.97361
0.91483
0.963230.924850.92569
0.972850.97267
0.969210.931101.00672
0.98592
0.98895
0.968391.03001
1.05837
1.07835
1.078021.11026
1.11054
1.12835
1.19765
1.20889
1.237481.28089
1.23266
1.27056
1.21578
1.156211.16001
1.045990.99035
1.00928
1.00827
0.97440
1.01728
1.07497
1.04155
1.06898

PL6t
1.00000
0.99373
1.02754
1.01174
0.99992
0.983390.965900.93295
0.97884
0.921420.930430.96467
0.97691
0.96688
0.93360
1.00979
0.97770
0.994930.97260
1.03566
1.04751
1.07413
1.10446
1.09147
1.11076
1.14190
1.20595
1.20877
1.243091.28484
1.22324
1.26938
1.18474
1.177141.17180
1.057540.98920
1.009391.00484
0.97225
1.02594
1.077191.04387
1.08631

Table A3: Land Price Indexes for Models 7-14
t
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

PL7t
1.00000
1.002451.03037
0.99704
1.00434
0.98205
0.975620.921700.96832
0.930020.94091
0.975430.970480.971960.941631.00029
0.99845
0.99277
0.97953
1.03172
1.06968
1.07515
1.07988
1.11303
1.12746
1.136211.203931.22165
1.244421.295171.23490
1.28560
1.22290
1.17110
1.16527
1.05617
0.99660
1.00818
1.006120.97594
1.02111
1.07266
1.04658
1.07361

PL8t
1.00000
1.01090
1.03702
1.005541.009090.99069
0.979170.929260.97006
0.93092
0.94415
0.973500.96972
0.96837
0.946140.99578
0.99687
0.993210.97349
1.025411.06459
1.07634
1.07917
1.11331
1.12879
1.143141.20416
1.219051.25027
1.29206
1.23590
1.27975
1.20928
1.16392
1.15226
1.052420.98956
1.000071.002310.96214
1.00718
1.05556
1.03255
1.05879

PL9t
1.00000
1.01292
1.03487
1.004621.00382
0.99359
0.977090.929070.97187
0.93577
0.952930.97551
0.97205
0.97328
0.94808
0.99728
0.997070.99575
0.977631.02813
1.06959
1.08072
1.08454
1.11888
1.13146
1.15034
1.21079
1.22756
1.256411.30048
1.24606
1.285141.21732
1.16572
1.15855
1.05050
0.996131.00491
1.00577
0.96592
1.01057
1.05708
1.03780
1.06364PL10t
1.00000
1.00101
1.02124
1.000231.01092
0.98811
0.96830
0.92076
0.96830
0.93984
0.94658
0.972210.96535
0.961390.938700.98952
0.984450.98498
0.96176
1.01653
1.058431.06978
1.07874
1.11199
1.12075
1.13941
1.19467
1.21229
1.24786
1.28978
1.233071.279371.20556
1.15735
1.15415
1.04062
0.99526
0.99335
1.001310.97429
1.01328
1.05184
1.03271
1.0521735

PL11t
1.00000
1.001111.020070.99957
1.00968
0.98858
0.96677
0.92111
0.96860
0.93767
0.94352
0.97030
0.964880.958360.935700.98997
0.98197
0.981800.96150
1.01355
1.055311.06841
1.07681
1.10909
1.11876
1.13519
1.18940
1.20788
1.244311.28683
1.22783
1.27498
1.20131
1.15470
1.14983
1.032200.99077
0.98698
0.997510.970121.010201.04489
1.027451.04833

PL12t
1.00000
1.001641.01684
0.991371.014280.981480.967470.924650.96908
0.94248
0.943140.967410.96458
0.95101
0.927200.98109
0.97465
0.976210.96500
1.01088
1.054121.065181.08001
1.10623
1.11938
1.14080
1.194471.20719
1.249971.28815
1.23456
1.26404
1.20324
1.15226
1.156531.03011
0.99864
0.99171
0.99182
0.962641.00866
1.04113
1.02551
1.03735

PL13t
1.00000
1.001451.01688
0.991411.01405
0.981400.96752
0.92460
0.96924
0.942450.942780.967610.96464
0.951120.92735
0.981220.97472
0.976140.96504
1.01092
1.054191.065341.080121.10614
1.119361.14095
1.194531.20730
1.24994
1.288091.23473
1.264021.20334
1.15209
1.15666
1.03001
0.99856
0.99186
0.99191
0.962611.00860
1.04124
1.02553
1.03739PL14t
1.00000
0.97977
1.00586
1.00403
0.99830
0.970220.94775
0.92279
0.966640.927210.92937
0.95536
0.95959
0.941110.92104
0.98288
0.952070.96866
0.95761
1.00430
1.03065
1.052441.08375
1.08280
1.10126
1.14218
1.18262
1.192691.240131.26915
1.208121.24084
1.17161
1.13840
1.14788
1.02186
0.981800.97776
0.98129
0.951440.99438
1.031311.011401.03911

Table A4: Model 12 Estimated Parameters
Coef
ϕ
∗
γ01
∗
γ11
∗
γ20
∗
γ21
∗
γ30
∗
γ31
∗
γ40
∗
γ41
∗
γ02
∗
γ12
∗
γ22
∗
γ32
∗
γ42
∗
γ52
∗
γ62
∗
γ72
∗
γ03
∗
γ13
∗
γ23
∗
γ33
∗
γ43
∗
γ53
∗
γ63
∗
γ73
∗
γ04
∗
γ14
∗
γ24
∗
γ34
∗
γ44
∗
γ54
∗
γ64
∗
γ74
∗
γ05
∗
γ15
∗
γ25
∗
γ35
∗
γ45

Estimate
1.1250−178.4700−0.4677
2.59255.34203.92693.74773.74362.70823.50994.83766.28254.44678.6903−1.35453.0835
11.1680
3.71054.6941
5.23659.05134.0106
3.5972
3.09662.58464.46444.53704.34535.25147.17143.48102.66942.70693.39973.5602
3.89135.07974.2398T stat108.12
−1.20
−0.88
2.90
37.90
13.32
34.44
4.59
6.93
6.44
35.22
42.02
37.70
29.12
−0.12
0.58
0.44
11.70
35.58
41.73
41.68
12.52
11.75
11.06
2.61
23.67
41.16
39.08
37.19
33.87
18.90
14.46
10.41
28.40
33.64
35.09
36.50
31.73

Coef
∗
γ55
∗
γ65
∗
γ75
∗
γ06
∗
γ16
∗
γ26
∗
γ36
∗
γ46
∗
γ56
∗
γ66
∗
γ76
∗
γ17
∗
γ27
∗
γ37
∗
γ47
∗
γ57
∗
γ67
∗
γ77
α∗2
α∗3
α∗4
α∗5
α∗6
α∗7
α∗8
α∗9
α∗10
α∗11
α∗12
α∗13
α∗14
α∗15
α∗16
α∗17
α∗18
α∗19
α∗20
α∗21

Estimate
2.00072.53933.03732.4371
3.63993.33942.6204
1.81892.53172.9012
1.17060.88902.2367
6.12672.16351.8749−0.78774.41021.00161.01680.99141.01430.98150.96750.92470.9691
0.9425
0.9431
0.96740.9646
0.95100.92720.98110.9747
0.97620.96501.01091.054136

T stat
11.40
13.87
8.29
10.57
22.20
25.68
17.32
9.70
16.33
12.77
1.05
0.42
2.56
3.57
6.72
4.88
−0.71
0.17
68.99
62.78
65.12
67.08
67.75
65.21
68.34
64.08
64.13
60.79
68.93
62.57
62.63
58.85
64.31
64.07
67.01
64.50
70.80
70.31

Coef
α∗22
α∗23
α∗24
α∗25
α∗26
α∗27
α∗28
α∗29
α∗30
α∗31
α∗32
α∗33
α∗34
α∗35
α∗36
α∗37
α∗38
α∗39
α∗40
α∗41
α∗42
α∗43
α∗44
λ∗0
λ∗2
λ∗3
λ∗4
τ∗
ρ∗
σ∗
µ∗0
µ∗2
µ∗3
δ∗
κ∗3
κ∗4
κ∗5
κ∗6

Estimate
1.06521.08001.1062
1.11941.14081.19451.20721.25001.2882
1.2346
1.26401.2032
1.1523
1.15651.0301
0.9986
0.9917
0.99180.96261.00871.04111.02551.0374
1.49701.12601.20500.9791−0.0130−0.0068
0.00401.10632.35191.67270.04161.14561.07590.90280.7627

T stat
70.67
71.14
72.99
72.94
74.26
74.79
75.94
75.85
75.82
71.35
72.84
66.80
65.75
62.33
52.86
50.28
53.61
62.16
57.74
61.79
63.60
65.01
65.20
41.09
34.84
33.81
14.61
−36.33−23.05
27.38
10.59
13.99
10.27
22.63
20.14
20.97
19.55
17.94

Table A5: Model 13 Estimated Parameters
Coef
ϕ
∗
γ01
∗
γ11
∗
γ20
∗
γ21
∗
γ30
∗
γ31
∗
γ40
∗
γ41
∗
γ02
∗
γ12
∗
γ22
∗
γ32
∗
γ42
∗
γ52
∗
γ62
∗
γ72
∗
γ03
∗
γ13
∗
γ23
∗
γ33
∗
γ43
∗
γ53
∗
γ63
∗
γ73
∗
γ04
∗
γ14
∗
γ24
∗
γ34
∗
γ44
∗
γ54
∗
γ64
∗
γ74
∗
γ05
∗
γ15
∗
γ25
∗
γ35
∗
γ45

Estimate
1.12500
0.00000
0.00000
2.61950
5.32700
3.91830
3.75460
3.760102.68760
3.48220
4.80540
6.27970
4.447508.684500.00000
3.04700
11.28900
3.71350
4.70260
5.23550
9.04860
4.01050
3.58740
3.09950
2.57700
4.46370
4.53470
4.344105.25070
7.16970
3.483202.66450
2.711403.399103.56030
3.89100
5.07840
4.23890

T stat
105.1
n.a.
n.a.
3.0
36.7
12.9
32.9
4.6
6.7
5.9
34.2
40.4
36.5
27.9
n.a
0.6
0.4
11.1
33.5
40.5
40.2
12.2
11.6
10.8
2.6
22.3
39.5
37.4
35.7
32.0
18.2
13.6
10.1
26.0
31.4
32.8
34.5
30.3

Coef
∗
γ55
∗
γ65
∗
γ75
∗
γ06
∗
γ16
∗
γ26
∗
γ36
∗
γ46
∗
γ56
∗
γ66
∗
γ76
∗
γ17
∗
γ27
∗
γ37
∗
γ47
∗
γ57
∗
γ67
∗
γ77
α2∗
α3∗
α4∗
α5∗
α6∗
α7∗
α8∗
α9∗
∗
α10
∗
α11
∗
α12
∗
α13
∗
α14
∗
α15
∗
α16
∗
α17
∗
α18
∗
α19
∗
α20
∗
α21

Estimate
1.999102.55230
3.01560
2.436403.638103.33900
2.62000
1.81930
2.536402.85420
1.322200.89163
2.23780
6.119502.186101.802400.00000
2.37400
1.00150
1.016900.991411.01400
0.981400.96752
0.92460
0.96924
0.942450.942780.967610.96464
0.951120.92735
0.981220.97472
0.976140.96504
1.01090
1.05420

37

T stat
10.5
13.1
7.6
9.9
19.8
24.0
16.0
9.2
15.3
12.3
1.1
0.4
2.4
3.5
6.4
4.5
n.a.
0.1
68.6
62.8
64.9
66.4
67.3
65.5
68.1
64.1
64.2
61.1
68.8
62.7
62.6
59.2
64.0
64.1
67.0
64.2
70.0
69.7

Coef
α∗22
α∗23
α∗24
α∗25
α∗26
α∗27
α∗28
α∗29
α∗30
α∗31
α∗32
α∗33
α∗34
α∗35
α∗36
α∗37
α∗38
α∗39
α∗40
α∗41
α∗42
α∗43
α∗44
λ∗0
λ∗2
λ∗3
λ∗4
τ∗
ρ∗
σ∗
µ∗0
µ∗2
µ∗3
δ∗
κ∗3
κ∗4
κ∗5
κ∗6

Estimate
1.06530
1.08010
1.10610
1.11940
1.14100
1.194501.20730
1.24990
1.288101.23470
1.26400
1.20330
1.15210
1.15670
1.03000
0.99856
0.99186
0.99191
0.962611.00860
1.041201.02550
1.03740
1.49740
1.12650
1.20520
0.97874−0.01303−0.00684
0.00402
1.10630
2.35450
1.672700.04166
1.14540
1.075700.90272
0.76241

T stat
69.7
70.0
72.2
71.7
73.2
72.9
74.5
73.8
73.9
70.0
71.5
66.5
65.1
62.8
54.1
51.3
54.8
62.1
58.6
62.1
63.8
64.5
65.1
40.8
35.3
35.6
15.3
−39.6
−22.7
26.3
10.0
13.3
10.0
18.4
18.5
19.3
17.8
16.3

Table A6: Model 14 Estimated Parameters
Coef
ϕ
α∗2
α∗3
α∗4
α∗5
α∗6
α∗7
α∗8
α∗9
α∗10
α∗11
α∗12
α∗13
α∗14
α∗15
α∗16
α∗17
α∗18
α∗19
α∗20
α∗21
α∗22
α∗23
α∗24
α∗25
α∗26
α∗27
α∗28

Estimate
1.155400.97977
1.00590
1.00400
0.99830
0.970220.94775
0.92279
0.966640.927210.92937
0.95536
0.95959
0.941110.92104
0.98288
0.952070.96866
0.95761
1.00430
1.03070
1.05240
1.08380
1.08280
1.10130
1.14220
1.18260
1.19270

T stat
96.9
64.3
59.6
61.2
62.6
63.8
61.9
63.8
60.6
60.4
58.3
65.0
58.9
59.5
56.1
60.5
60.7
63.6
60.7
66.2
65.9
65.8
66.1
68.5
68.0
69.2
69.0
70.7

Coef
∗
α29
∗
α30
∗
α31
∗
α32
∗
α33
∗
α34
∗
α35
∗
α36
∗
α37
∗
α38
∗
α39
∗
α40
∗
α41
∗
α42
∗
α43
∗
α44
ω1∗
ω2∗
ω3∗
ω4∗
ω5∗
ω6∗
ω7∗
ω8∗
ω9∗
∗
ω10
∗
ω11
∗
ω12

Estimate
1.240101.26920
1.208101.24080
1.17160
1.13840
1.14790
1.021900.981800.97776
0.98129
0.951440.99438
1.03130
1.011401.039107.77560
5.39740
7.42060
5.569405.69080
4.77040
3.14740
3.71300
5.28570
6.49860
4.81450
5.76630

38

T stat
69.6
70.2
66.0
67.6
62.6
61.8
58.8
51.7
48.3
51.9
58.7
55.5
58.6
60.3
60.8
61.4
11.7
8.7
40.1
40.6
41.1
20.5
21.2
25.5
40.4
41.6
41.1
41.7

Coef
∗
ω13
∗
ω14
∗
ω15
∗
ω16
∗
ω17
∗
ω18
∗
ω19
∗
ω20
∗
ω21
∗
ω22
∗
ω23
∗
λ0
λ∗2
λ∗3
λ∗4
τ∗
ρ∗
σ∗
µ∗0
µ∗2
µ∗3
δ∗
κ∗3
κ∗4
κ∗5
κ∗6

Estimate
7.01990
4.575104.82300
4.99440
3.57980
3.12610
3.839104.076102.46420
2.79550
2.99060
1.46500
1.11790
1.205400.93182−0.01363−0.00945
0.00393
1.24560
2.57690
1.697700.04024
1.10660
1.041400.85249
0.73501

T stat
40.9
39.1
41.4
38.0
25.8
21.9
34.0
38.2
27.4
25.2
28.9
38.7
33.9
34.7
14.3
−40.6
−43.6
26.2
10.4
13.0
9.4
18.3
18.5
19.3
18.2
17.0

Table A7: Overall Property Price Indexes for Models 7-15
t
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

P7t
1.00000
1.00085
1.02558
0.99679
1.00195
0.98255
0.976210.926240.96803
0.93363
0.94298
0.97477
0.969260.97001
0.94357
0.99632
0.994650.98899
0.97747
1.02446
1.05930
1.06477
1.06913
1.099311.11200
1.12146
1.18379
1.200051.22028
1.26637
1.212311.25840
1.200141.15480
1.151441.05406
0.99992
1.010881.01078
0.98161
1.02323
1.06985
1.04683
1.07165

P8t
1.00000
1.00845
1.03182
1.00434
1.006310.990210.979200.93260
0.969390.93392
0.94553
0.97282
0.968430.96669
0.947210.99216
0.993150.98933
0.97174
1.01891
1.05513
1.06639
1.069011.10035
1.11415
1.12868
1.18522
1.19898
1.22718
1.26535
1.21459
1.25478
1.18908
1.149041.140191.050140.99258
1.002631.006250.96775
1.00961
1.05379
1.033311.05763P9t
1.00000
1.00949
1.02850
1.003171.00097
0.99266
0.97732
0.93333
0.97086
0.93903
0.95376
0.974510.970240.97068
0.949310.99266
0.992470.99068
0.97505
1.01975
1.057311.06788
1.07136
1.10220
1.11297
1.13160
1.18652
1.20171
1.22703
1.26639
1.21843
1.25352
1.19125
1.14690
1.14265
1.04768
0.99872
1.007141.009850.97202
1.01282
1.05435
1.03785
1.06135P10t
1.00000
0.99938
1.017030.99949
1.00730
0.987800.969430.92566
0.96774
0.94262
0.948120.97169
0.964410.960290.94094
0.986020.98147
0.98135
0.96100
1.009901.04807
1.05887
1.06707
1.097101.10449
1.12295
1.173421.18946
1.22104
1.25869
1.208291.250111.182191.14051
1.139641.039220.99792
0.99688
1.00584
0.979641.015411.04994
1.03344
1.05123P11t
1.00000
0.99952
1.01615
0.998961.00635
0.98825
0.96805
0.925880.968100.94067
0.94538
0.96999
0.96401
0.95762
0.93829
0.986540.97936
0.978620.96086
1.00738
1.045481.05789
1.06560
1.094811.10305
1.11950
1.169021.18586
1.218311.256621.20404
1.24667
1.17877
1.13833
1.13588
1.03148
0.99355
0.990811.002070.97553
1.01229
1.04330
1.02837
1.0474639

P12t
1.00000
1.00004
1.01344
0.991761.01043
0.98182
0.968620.928900.96850
0.94489
0.94495
0.96738
0.96375
0.951070.93059
0.97875
0.97294
0.973780.96415
1.005211.04480
1.05536
1.06902
1.09275
1.104211.12518
1.17434
1.185971.22430
1.25878
1.21096
1.23771
1.18123
1.13665
1.14229
1.02968
1.00047
0.994900.99677
0.96856
1.010911.04003
1.02667
1.03754P13t
1.00000
0.99988
1.01347
0.99179
1.01023
0.98175
0.96867
0.92886
0.968650.944880.944640.96756
0.96382
0.951170.93073
0.97888
0.97301
0.97372
0.964191.00526
1.04487
1.05552
1.069141.09268
1.104211.12532
1.174401.186071.224291.25873
1.21113
1.23771
1.18133
1.13651
1.142421.02960
1.000400.99504
0.996850.968531.01086
1.040141.02670
1.03758

P14t
1.00000
0.98103
1.00391
1.00301
0.99662
0.97234
0.951530.92799
0.966850.93178
0.93316
0.95706
0.95974
0.94272
0.925700.98075
0.953070.967280.95787
0.99945
1.02350
1.043811.07255
1.071221.08744
1.12597
1.16287
1.17204
1.214401.24039
1.185991.21555
1.15166
1.123211.13354
1.02197
0.98529
0.98235
0.98735
0.95858
0.997941.030961.01379
1.03911P15t
1.00000
0.99032
0.994961.00487
0.993110.96075
0.958440.92797
0.93849
0.915440.920180.93083
0.92352
0.92530
0.91027
0.948290.92278
0.949440.94630
0.963810.99846
0.99253
1.02813
1.03585
1.049841.07056
1.12662
1.120811.17653
1.19156
1.16233
1.18834
1.12278
1.094111.07889
1.007410.959650.95935
0.95880
0.94232
0.967640.98085
0.98602
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42