Abstract:
Asset indexes are intended to track changes in the market value of assets. However, some assets such as real estate can change in value for reasons other than that a general change in the market has occurred. For example, a house can have an addition which adds another bedroom or a portion of land can be sub-divided, in which case an increase or decrease respectively in the asset value can occur which is independent of movements in the overall market. A method of generating an asset index under these circumstances is disclosed by effectively removing from the asset portfolio those assets which have had a change in attribute come to notice on the day the asset index is calculated. Such an asset is restored to the portfolio on subsequent days.

Description:
[0001]    The present invention relates to computer generated digitally encoded electric waveforms and their use in the implementation of financial methods and systems. In the preferred embodiment of the invention to be described hereafter, the description is primarily concerned with the calculation of daily indices in relation to residential property, however, the invention is not restricted to this asset class and is applicable to other types of assets such as motor vehicles. 
       COPYRIGHT NOTICE 
       [0002]    This patent specification contains material which is subject to copyright protection. The copyright owner has no objection to the reproduction of this patent specification or related materials from associated patent office files for the purposes of review, but otherwise reserves all copyright whatsoever. 
       TECHNICAL FIELD OF THE INVENTION 
       [0003]    The present invention relates to computer generated digitally encoded electric waveforms and their use in the implementation of financial methods and systems. In the preferred embodiment of the invention to be described hereafter, the description is primarily concerned with the calculation of daily indices in relation to residential property, however, the invention is not restricted to this asset class and is applicable to other types of assets such as motor vehicles. 
       BACKGROUND ART 
       [0004]    Most individuals own assets which may be conveniently categorised into three classes. The first class is fixed physical assets such as real estate, predominantly the residential home of the individual. The second class is moveable physical assets, such as motor vehicles or other items of equipment. The third class of assets are financial assets such as shares in listed companies, bonds and the like. 
         [0005]    It is useful to know how the values of such assets change over time. In relation to the abovementioned financial assets such as listed company shares and bonds, there is a wealth of financial data. In addition to historical data there is also provided what might be termed “synthetic products” such as a stock exchange index which tracks the overall price movement of the exchange. In Australia such an index is known as the ASX, the USA has the Dow Jones and the Standard &amp; Poors, Britain has the Financial Times Index (or “Footsie”), and so on. In addition to price (or capital gain/loss) indices, there are also accumulation indices which indicate the total returns taking into account both capital gains (or losses) and revenue received by way of dividends. In addition there is a raft of financial products available including options, derivatives and the like which are tailored towards various persons and corporations which have specific needs or uses for such financial instruments. 
         [0006]    However, in respect of non-financial assets there are few equivalents available. For example, in relation to motor vehicles, whilst there is data as to new vehicle prices and also data as to the price of used vehicles as to age, it is not immediately apparent from any of these price indications as to whether or not the rate of depreciation of motor vehicles is changing, for example. Such information would be of interest to corporations having fleets of motor vehicles, and even to an individual contemplating making a decision as to whether to retain an existing vehicle, to sell the existing vehicle and purchase a new vehicle, or to sell the existing vehicle and purchase a used vehicle. A similar comment applies in relation to fixed assets such as real estate. 
         [0007]    The genesis of the present invention is a desire to at least ameliorate the abovementioned situation by the provision of, or generation of, asset indices. 
         [0008]    The generation of asset indices in the modern world requires the use of computers. Whilst it is theoretically possible to manually generate an asset index (over a short period of time at least) using “paper and pencil” or paper and calculator” methods, in practice the volume of data is overwhelming and so modern computers must be utilised. This transforms the problems involved in the creation of such an asset index into the realm of electrical engineering. It is the solution of the electrical engineering problems inherent in the generation of an asset index that is the subject of the present invention. 
         [0009]    The present applicant has filed an International Patent Application No. PCT/AU2008/000244 (published under No. WO 2008/104018) (Attorney Ref 5097I-WO), which corresponds to U.S. application Ser. No. 12/527,832, and Australian Patent Application No. 2010 201 821 (Attorney Ref 5097L-AU), all of which disclose, amongst other things, the generation of a real estate index. In the preferred embodiment of the present invention, the methods disclosed in the above-mentioned patent specifications are, or can be, utilised. Accordingly, the disclosures of all of the abovementioned patent applications is hereby incorporated by the present specification for all purposes. 
         [0010]    The present invention is particularly concerned with overcoming various difficulties in the production of such indices brought about by changes of a capital nature such as expenditure on additions to housing, rather than changes in the actual market price of such housing. 
       SUMMARY OF THE INVENTION 
       [0011]    In accordance with a first aspect of the present invention there is disclosed a method of calculating an index of price movements of a class of assets, which index is substantially independent of price movements of a capital nature, said method comprising the steps of: 
         [0000]    (i) storing historical time of sale data, historical price data and historical asset characterisation data regarding previous asset sales of assets in said class of assets,
 
(ii) inputting a tranche of current data regarding current sales of assets in said class of assets, said current data including current price data and current asset characterisation data,
 
(iii) comparing said current asset characterisation data with said historical asset characterisation data to identify specific assets which have been sold previously and re-sold currently,
 
(iv) comparing the asset characterisation data of said specific assets to determine those specific assets, if any, which have undergone a change of a capital nature between said previous sale and said current re-sale, and
 
(v) excluding said capital change specific assets from said index generation.
 
         [0012]    In accordance with a second aspect of the present invention there is disclosed a method of generating a digitally encoded electric signal to represent an index of price movements of a class of assets, which index is substantially independent of price movements of a capital nature, said method comprising the steps of: 
         [0000]    (i) storing in a data storage means historical time of sale data, historical price data and historical asset characterisation data regarding previous asset sales of assets in said class of assets,
 
(ii) inputting into said data storage means a tranche of current data regarding current sales of assets in said class of assets, said current data including current price data and current asset characterisation data,
 
(iii) comparing, in a comparator means connected with said data storage means, said current asset characterisation data with said historical asset characterisation data to identify specific assets which have been sold previously and re-sold currently,
 
(iv) comparing in said comparator means the asset characterisation data of said specific assets to determine those specific assets, if any, which have undergone a change of a capital nature between said previous sale and said current re-sale,
 
(v) computing said index whilst excluding said capital change specific assets from said computation, and
 
(vi) outputting said digitally encoded electric signal to represent said index.
 
         [0013]    Preferably the index is calculated on each occasion of a successive sequence of occasions, and said excluding step (v) comprises: 
         [0000]    (vi) excluding the capital change specific assets from a portfolio assets used in the current index calculation to calculate a current portfolio value,
 
(vii) re-calculating the value of the same portfolio of assets on the occasion immediately preceding the current occasion,
 
(viii) summing the current portfolio values, summing the immediately preceding portfolio values and dividing the sum of the current portfolio values by the sum of the immediately preceding portfolio values to create the index increment for the current index calculation.
 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]    A preferred embodiment of the present invention will now be described with reference to the accompanying drawings in which: 
           [0015]      FIG. 1  is a flow chart illustrating the production of a capital gains index, 
           [0016]      FIG. 2  is a similar flow chart illustrating the production of an accumulation index, 
           [0017]      FIG. 3  is a similar flow chart illustrating the initial production of a self-financing index, 
           [0018]      FIGS. 4A and 4B  are a two page flow chart illustrating the production of a self-financing index on a subsequent date, 
           [0019]      FIG. 5  is a similar flow chart illustrating the production of a self-financing index including rental data, 
           [0020]      FIG. 6  is a block diagram of a computer system on which embodiments of the present invention can be implemented, and 
           [0021]      FIG. 7  is a representation of a digitally encoded electric waveform. 
       
    
    
     DETAILED DESCRIPTION 
       [0022]    In the preferred embodiment, the following problem is addressed. A particular city, or collection of cities, has a stock of housing (which can be freestanding houses, apartments (or units or condominiums) or the like) which is continually being modified by their owners so that the housing is upgraded, for example by the addition of an ensuite bathroom, by the addition of one or more extra bedrooms, and so on. These additions represent the input of additional capital by the owners and result in the value of the property being increased. This change in the structure of the house comes to public notice when the house is sold, or if the house is offered for rent. 
         [0023]    For example, if the house is sold, then the value of the house has changed (in general increased) from its last sale by two components. One of these is the change in the market the housing, which the index should reflect, and the other is the change in the structure of the house which should not distort the index. 
         [0024]    In brief, the way in which the computations required to calculate the index is modified to take into account this change. If the minimum time period between changes of the index is one day, then on the day (day n) where the sale would normally be added to the index, thereby bringing to light the change in the structure of the house, then that particular house is deleted from the index computation from both that day (day n) and the previous day (day n−1). As a consequence, the change in the index from day n−1 to day n is not distorted by the particular sale with the unknown contribution brought about by the alterations to the house. However, when the index comes to be computed for the next day (day n+1), then the house with the alterations is included on both day n and day n+1 for the calculation of the index for day n+1. 
         [0025]    Because the index computation excludes additional capital used in the construction of the housing addition the index, and the portfolio it represents, are said to be self-financing. 
         [0026]    First the construction of indices will be discussed and then their “conversion” into self-financing indices. 
       Index Construction 
       [0027]    In the preferred embodiment residential property indices are calculated, on a daily basis, for the cities of Sydney, Melbourne, Brisbane, Adelaide, Perth, and all of Australia. 
         [0028]    For each of the above, there will be 3 indices: (1) houses, (2) units (condominiums or apartments) and all dwellings, and (3) a stock weighted average of the house and unit indices. The dwelling stock figures are from the 2006 Australian census. There is no intention to alter the weights until the 2011 Australian census results are published. 
         [0029]    Thus, there will be a total of 18 index series. 
         [0030]    The intention of synthetic financial contracts is to represent and mirror as closely as possible returns on the physical asset class (eg monies), but in an idealized market where the asset class is infinitely liquid. An investor would ideally wish to construct a portfolio exactly representing a given market segment. Income from the portfolio would then represent an overall yield from the market segment. The investor would also wish to rebalance their portfolio each period to track changes in market composition. 
         [0031]    Thus, returns on the underlying index should equate to returns generated by the above strategy: forming a portfolio, holding it for one period, booking the income yield and capital gain or loss, and then rebalancing the portfolio. 
         [0032]    In the case of tradable property indices, there should be capital gain indices and index contracts should pay: 
         [0000]    1. The value of the capital gain index at the expiry date.
 
2. A periodic income equal to a rental yield on the properties composing the index.
 
         [0033]    Each of the city indices will represent capital value in the sense that
   1. They will be based at the mean property value in $000 of the market segment which they represent.   2. Daily returns on each index will equal daily returns on a portfolio of the properties composing the market segment represented.   
 
         [0036]    The All Australia indices will be calculated as a stock weighted average of the corresponding 5 city indices. 
         [0037]    Additionally, a daily imputed rental yield will be calculated on each index. 
       Calculation of the Indices 
     Summary 
       [0038]    The indices will firstly represent capital value: daily returns on each index will equal the best estimate of 1 day capital gains on the portfolio of properties represented by the index. 
         [0039]    The index calculation is in four parts (see Section 2.2 for details):
   1. Value every property in the given category.   2. Form a portfolio of properties which represents the index from the category.   3. Calculate the change in value of that portfolio over the subsequent period ie. 1 business day.   4. Multiply the previous index value by the change in portfolio value.   
 
         [0044]    The calculation to value every property is in six parts:
     1 . Enter the relevant data adjust (that is, inflate or deflate).     2 . Inflate or deflate all observed historical sale prices to a given fixed date via multiplication by the relative hedonic index for the property type (house/unit) and statistical subdivision (SSD) in which the property is located. See Section 2.3 for details of the hedonic index calculation method. Instead of the hedonic index method a median price index can be used instead.     3 . Transform the inflated sale price data by a fixed, monotonically increasing function.     4 . Fit the transformed data to a set of explanatory variables via a generalized additive model to obtain a valuation function for each property type and SSD.     5 . For all properties in the current population, apply the appropriate valuation function to obtain transformed value estimates.     6 . Apply the inverse of the transformation function in step  3  to the transformed value estimates in step  4  to obtain the actual property value estimates.   
 
         [0051]    These steps are indicated as steps  1 - 6  in  FIG. 1  of the drawings. 
         [0052]    See Section 2.4 for details of steps  2 - 5  above. 
       Section 2.1 
     Capital Gain Index Calculation 
       [0053]    For each property type (house/unit) and city, the method of calculation is to: 
         [0054]    On the initial (base) day t=0:
   1. Value every property in the given category, preferably by the hedonic imputation method.   2. Sort the properties by value.   3. Form a “market portfolio” consisting of all properties between the 5 th  and 95 th  percentiles. The upper and lower tails are preferably removed for statistical reasons: namely to ensure that the behaviour of outliers does not influence the dynamics of the index away from the dynamics of the actual market. The mean value of this portfolio divided by 1000 is the initial value of the index I 0 .   4. Find the total value of all the properties in the market portfolio. Call this value M 0 .   On each subsequent day t&gt;0:   1. Value every property in the given category by the hedonic imputation method (see Sections 2.3 and 2.4 below).   2. Find the new total value of all the properties in the previous day&#39;s market portfolio. Call this value M.   3. Since the value of M t-1  is known from the previous day, the change in value of the portfolio from the previous day is M t */M t-1 . The new middle index value is then I t =(M t */M t-1 )I t-1 .   4. Sort all the properties by value.   5. Form a new market portfolio consisting of all properties between the 5 th  and 95 th  percentiles after the sort.   6. Calculate the total value of all the properties in the new middle portfolio. Call this value M t .   
 
       Section 2.2 
     Hedonic Index Calculation 
       [0066]    A hedonic variable is an observable attribute of a good such that variation in the value of the attribute is explanatory of some of the variation in the price of the good. In the case of residential property prices, examples of hedonic attributes are the suburb (location), and land size and the number of bedrooms. 
         [0067]    A hedonic property value index is calculated from observed sale prices, taking into account the hedonic attributes of the properties which sold during each observation period. If there is an index series I 0 , I 1 , . . . , I t , the relative value I t     2   /I t     1    between any two periods is the least squares error estimate of the mean relative value over the time interval t 1 →t 2  of the properties in the population, conditional on observing the prices and hedonic attributes of those properties which actually sold during the period of construction of the index. 
         [0068]    Given hedonic variables X 1 , . . . , X n , an adjacent period hedonic formula is applied to property sales P i  in each pair of periods (T k-1 ,T k ). In our case, each period T k  is a calendar month. 
         [0000]    
       
         
           
             
               
                 
                   
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         [0000]    where:
       The ƒ j  are transformations of the hedonic variables.   The c j  are time varying numerical coefficients.   The S j  are dummy variables with S j =1 if property i is in suburb j.   The s j  are time varying numerical coefficients of the suburb dummy variables.   τ 1  is a dummy variable with τ 1 =1 if the sale occurred in period T k  and τ 1 =0 otherwise.   ε k  is the (zero mean) residual error term       
 
         [0075]    The above hedonic model thus gives the best estimate of the log return on a property, controlling for its most statistically significant, objectively observable price determining attributes. For further information reference is made to the abovementioned cross-referenced PCT specification. 
         [0076]    The coefficient λ 1  gives the hedonic index log returns over the period (T k-1 , T k ). That is, if H(T) is the index value at time T, then 
         [0000]        H ( T   k-1 )=exp{λ 1 ( T   k )+σ 1 ( T   k ) 2 /2 }H ( T   k-2 )  (2.2)
 
         [0000]    where σ 1 (T k ) is the standard error of λ 1 (T k ). This is preferable adjustment term to make the index track returns on a portfolio. 
         [0077]    The hedonic index thus obtained is a capital gain index. 
         [0078]    The average return on the hedonic property index over a period [t 0 , t] is therefore an estimate of the average return on a diversified property portfolio over [t 0 , t]. 
         [0079]    The price adjustment may also be termed “benchmarking”. The actual imputation index uses an adjacent period index, which is disclosed in Australian Patent Application No. 2008 200 879 (Attorney Ref. 5097′-AU). However, it is possible to use any other different index calculation method to calculate the benchmark index. 
         [0080]    There is an additional feature of the use of the benchmark index in the preferred embodiment, whereby a benchmarking index calculated on periods of length T (such as a month) may be adapted as a benchmark for an imputation index with increments over the shorter time interval D (such as a day). 
         [0081]    Let n=T/D. Then calculate n benchmarking indices of period T, beginning at the time t=0, D, 2D, . . . , (n−1)D. 
         [0082]    For a given time interval τ k =[kD, (k+1)D], find the n time intervals T 1 , . . . , T n  of length T in each of the above benchmarking indices which each contain the shorter time interval τ k . 
         [0083]    Take the increments λ 1 , . . . λ n  corresponding to the time intervals T 1 , . . . , T n  from each of the n period T benchmarking indices which contain the shorter time interval τ k . 
         [0084]    The increment Λ k  for period τ k  in the new benchmarking index of period D is the average of the λ 1 , . . . , λ n , that is: Λ k =(λ 1 , . . . , λ n )/n. 
         [0085]    The adapted period D benchmark index value at time t=kD is then either B(t)=B(0)+Λ 1 + . . . +Λ k  or B(t)=B(0)exp{Λ 1 + . . . +Λ k }, depending on whether or not the original input (sales price) data was transformed prior to calculation of the benchmarking indices. 
         [0086]    The transformations ƒ j  are determined in the following manner: 
         [0087]    Let input variable  1  be the land size, with c 1  its coefficient and ƒ 1  its transformation. 
         [0088]    The objective function 
         [0000]    
       
         
           
             
               
                 
                   
                     
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         [0000]    is used to begin. 
         [0089]    That is, only the landsize and suburb are regressed against the log of observed price. Various functional forms of ƒ 1  have been tested, with the best ie. the one which minimises the standard deviation of the error ε k  found to be ƒ 1 (x)=log x. 
         [0090]    For each of the input variables i with a non-binary domain, let the range of observable values bε i,1 , . . . , x i,n     i   . For j=1, . . . , n i , let χ i,j  be a dummy variable with χ i,j =1 if X i =x i,j . 
         [0091]    For each i&gt;1, determine the coefficients γ i,j  in the regression: 
         [0000]    
       
         
           
             
               
                 
                   
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         [0092]    Each transformation function ƒ i , i&gt;1 is then completely defined by ƒ i (x i,j )=γ i,j . 
         [0093]    Thus, each transformation ƒ i , i&gt;1 is determined by regressing suburb and log (landsize) with dummy variables describing the possible values of the input X i  against log of observed price, one input variable X i  at a time. 
         [0094]    The set of objectively observable attributes X 1  . . . , X n  in the hedonic index preferably are:
       Suburb   Landsize (m 2 ) if property type=house   Floorsize (m 2 ) if property type=unit   Street type eg. highway, main road, suburban street, cul de sac etc.   Property type eg. free standing house, semi-detached house, low rise apartment, high rise apartment   Construction type eg. brick, timber, weatherboard   Number of bedrooms   Number of bathrooms   Ratio of bedrooms/bathrooms   Number of carspaces   Pool (Y/N)   Waterfront (Y/N)   Air Conditioning (Y/N)   View (Y/N)       
 
       Section 2.3 
     Valuation of Individual Properties 
       [0109]    As discussed in Section 2.1, the valuation of each property in the population is a 5 step process. It will be recalled that a different model is preferably fit for each property type (house/unit) and each statistical sub-division or SSD (same general mathematical structure for all models, but different parameter values). 
         [0110]    Thus in what follows, it is assumed that all properties are of the same type (house/unit) and in the same SSD (although any geographical region or market segment can be used, for example a local government area, a suburb or houses above, say, $2 million in price): 
         [0111]    Each observed property sale at price P i  and time T i  will have hedonic attribute values {tilde under (x)} i  where {tilde under (x)} is an evaluation of {tilde under (X)}=(X i , . . . , X n ). Note that some elements of {tilde under (x)} i  may be NULL if there is missing data, for example if the number of bathrooms is not known. 
         [0112]    Suppose the current time is T t .
   1. Adjust (ie. inflate or deflate) all observed sale values by the relative hedonic index value:   
 
         [0000]        {tilde over (P)}   i =( I   t   /I   i ) P   i   (2.5)
   2. Transform the inflated sale prices by a suitable continuous, monotonically increasing function:   
 
         [0000]        Y   i =Φ( {tilde over (P)}   i )  (2.6)
   3. Fit a generalized, additive model (GAM) Ψ: (X 1 , . . . , X n )         Y which maps observed hedonic attribute data to the transformed prices.   
 
         [0116]    The set of hedonic observables in the GAM are those in the hedonic index plus:
       Month of observed sale   Most recent previous sale of the property, adjusted (typically inflated) by the relative hedonic index   Sales of nearest neighbours, adjusted (inflated or deflated) by the relative hedonic index   Latitude and longitude of property       
 
         [0121]    Nearest neighbours are taken to be those properties closest in distance with the same number of bedrooms. If the number of bedrooms in the observed sale property is not known, the nearest neighbours are just those closest in distance, ignoring the number of bedrooms. 
         [0122]    Separate models are used for properties with an unknown number of bedrooms, unknown number of bathrooms or missing values for other attributes. 
         [0123]    Note that not all properties have records of previous sales, so this hedonic variable can be NULL. 
         [0124]    Conceptually, the GAM is then: 
         [0000]    
       
         
           
             
               
                 
                   
                     
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                           n 
                         
                          
                         
                             
                         
                          
                         
                           
                             c 
                             jk 
                           
                            
                           
                             S 
                             j 
                           
                            
                           
                             
                               f 
                               k 
                             
                              
                             
                               ( 
                               
                                 x 
                                 k 
                               
                               ) 
                             
                           
                         
                       
                     
                     + 
                     
                       ɛ 
                       i 
                       0 
                     
                   
                 
               
               
                 
                   ( 
                   2.7 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where:
       The ƒ k  are transformations of the hedonic variables.   The c jk  are numerical coefficients.   The S j  are dummy variables with S j =1 if property i is in suburb j.   ε i   0 =Y i −Ŷ i   0  is the (zero mean) residual error term       
 
         [0129]    The residuals ε i   0  are then fit by least squares to a 2-D cubic spline function of the latitude and longitude (u i , v i ): 
         [0000]      ε i   0   =g ( u   i   ,v   i )+ε i   (2.8)
 
         [0130]    The final model is then 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       Y 
                       ^ 
                     
                     i 
                   
                   = 
                   
                     
                       c 
                       0 
                     
                     + 
                     
                       
                         ∑ 
                         
                           j 
                           = 
                           1 
                         
                         m 
                       
                        
                       
                           
                       
                        
                       
                         
                           ∑ 
                           
                             k 
                             = 
                             1 
                           
                           n 
                         
                          
                         
                             
                         
                          
                         
                           
                             c 
                             jk 
                           
                            
                           
                             S 
                             j 
                           
                            
                           
                             
                               f 
                               k 
                             
                              
                             
                               ( 
                               
                                 x 
                                 k 
                               
                               ) 
                             
                           
                         
                       
                     
                     + 
                     
                       g 
                        
                       
                         ( 
                         
                           
                             u 
                             i 
                           
                           , 
                           
                             v 
                             i 
                           
                         
                         ) 
                       
                     
                     + 
                     
                       ɛ 
                       i 
                     
                   
                 
               
               
                 
                   ( 
                   2.9 
                   ) 
                 
               
             
           
         
       
       
         4. Given a particular property with observed hedonic attributes (x 1 , . . . , x n , u, v), obtain the transformed price estimate from the GAM: 
       
     
         [0000]        Ŷ =Ψ( x   1   , . . . ,x   n   ,u,v )  (2.10)
   5. The GAM price estimate is then   
 
         [0000]        {circumflex over (P)}=Φ   −1 ( Ŷ )  (2.11)
 
         [0133]    Steps 4 and 5 above are applied to all properties in the relevant population to obtain the list of values in Step 1 of Section 2.2. 
       Section 2.4 
     Calculation of Rental Yields Introduction 
       [0134]    Rental yields are calculated daily. The yield is intended to represent the rental income for the given day as a proportion of total property market value if all properties in the population were rented without any market dilution effects. 
         [0135]    Clearly only a minority of properties are actually rented at any given time and the majority of those were rented during a prior period. 
         [0136]    The figures in the following Table I are from the 2006 Australian Census: 
         [0000]    
       
         
               
               
               
               
               
             
               
             
               
               
               
               
               
             
               
             
               
               
               
               
               
             
           
               
                   
                 Table I 
               
               
                   
                   
               
               
                   
                 City 
                 % Owned 
                 % Mortgaged 
                 % Rented 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                 Houses 
               
             
          
           
               
                   
                 Sydney 
                 40.2% 
                 41.0% 
                 18.8% 
               
               
                   
                 Melbourne 
                 40.4% 
                 42.4% 
                 17.2% 
               
               
                   
                 Brisbane 
                 34.0% 
                 42.4% 
                 23.6% 
               
               
                   
                 Adelaide 
                 39.9% 
                 42.7% 
                 17.4% 
               
               
                   
                 Perth 
                 34.6% 
                 45.0% 
                 20.4% 
               
             
          
           
               
                 Units 
               
             
          
           
               
                   
                 Sydney 
                 18.1% 
                 21.8% 
                 60.1% 
               
               
                   
                 Melbourne 
                 18.6%  
                 19.0%  
                 62.4% 
               
               
                   
                 Brisbane  
                 15.3%  
                 16.3% 
                 68.4% 
               
               
                   
                 Adelaide  
                 21.1%  
                 17.9% 
                 60.9% 
               
               
                   
                 Perth 
                 20.2% 
                 20.5%  
                 59.4% 
               
               
                   
                   
               
             
          
         
       
     
         [0137]    Consequently, a hedonic rental formula, essentially a “rental automatic valuation method” is derived from observed rents and the attributes of the properties which do rent. This formula is then applied to impute rental income for those properties not being rented (because they are owner occupied). 
         [0138]    The rental yield for each index is the sum of the imputed rental incomes for every property in the population divided by the sum of the imputed property values derived by the method of Section 2.3. The rental income for each index linked note is then that day&#39;s rental yield multiplied by the day&#39;s index value. 
       Rental Yield Calculation 
       [0139]    The method of imputing rents for all properties in the population is first described with reference to steps  15 - 20  of  FIG. 2 . 
         [0140]    As with valuation of all properties in Section 2.2, a different rental model is fit for each property type (house/unit) and each SSD (same general mathematical structure for all models, but different parameter values). Thus in what follows, it is assumed all properties are of the same type (house/unit) and in the same SSD. 
         [0141]    All rental listings (most recent listing price) occurring in the 6 months prior to the calculation date are used. The reason for the 6 months time period is that properties advertised for rent during this time period can reasonably be assumed to be currently rented at the same rate. The most recently advertised rent is used where confirmation of the actual rent is not available, which is usually the case. All rents are converted to an annual figure, counting 365 days in a year. 
         [0142]    Suppose there are N properties in the population, for which we have value estimates {circumflex over (P)} 1 , . . . , P N , by the method of Section 2.3. Suppose we have observed rental information R 1 , . . . , R M  for M&lt;N of these properties. 
         [0143]    We use the same hedonic variables listed in Section 2.3 and the method of Section 2.4 to fit a hedonic imputation model for the rental income from any property, given its hedonic attributes (without needing to inflate or deflate the rents as in step  2  of  FIG. 1 ): 
         [0000]        {circumflex over (R)}=Φ   rent   −1 (Ψ rent ( x   1   , . . . ,x   n ))  (3.1)
 
         [0000]    where (x 1  . . . , x n ) is the vector of observed hedonic attributes. 
         [0144]    We thus obtain a full set of N rental estimates {circumflex over (R)} 1 , . . . , {circumflex over (R)} N  for all properties in the population. 
         [0145]    The annualized yield for the given day is then 
         [0000]    
       
         
           
             
               
                 
                   
                     y 
                     t 
                   
                   = 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       N 
                     
                      
                     
                         
                     
                      
                     
                       
                         
                           R 
                           ^ 
                         
                         i 
                       
                       / 
                       
                         
                           ∑ 
                           
                             i 
                             = 
                             1 
                           
                           N 
                         
                          
                         
                             
                         
                          
                         
                           
                             P 
                             ^ 
                           
                           i 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   3.2 
                   ) 
                 
               
             
           
         
       
     
         [0146]    The day&#39;s rental income per index is then 
         [0000]      rent= y   t   I   t /365  (3.3)
 
         [0000]    where I t  is that day&#39;s index value. 
         [0147]    Turning now to  FIGS. 3-5 , in  FIG. 3  the steps  31 - 38  required for the calculation of the asset index for a base date are set out. These mirror steps  1 - 9  of  FIG. 1 . At a subsequent date, the index is calculated carrying out the steps  41 - 56  as set out in  FIGS. 4A-4B  which flow over two sheets. Steps  41 - 47  of  FIG. 4A  mirror steps  31 - 37  of  FIG. 3 , however, in steps  48 - 51  the current period&#39;s market portfolio is amended by deleting any assets which have had a value of an attribute change since the previous period. Thus a house which has had a bedroom added had since it was last sold, will be deleted from the market portfolio. Thereafter, steps  52 - 56  are carried out using the adjusted portfolio to obtain the current period&#39;s index value. 
         [0148]    On the next occasion on which the index value is calculated, normally the next day but conceivably the next week or month, steps  41  to  56  are repeated. However, on this occasion the specific assets which have had an attribute change in the previous period will not have had that same, or any, attribute change in the current period. Therefore those assets which were excluded on the previous occasion, will be included on the next occasion. 
         [0149]    In order to produce an accumulation index including rental income, the steps  501 - 509  of  FIG. 5  are carried out. These steps mirror the steps  15 - 22  of  FIG. 2  save that again the asset or property portfolio excludes those assets in which an attribute changed. This exclusion arises easily because the work carried out in previous steps  48  and  49  of  FIG. 4A  is effectively repeated in step  507 . 
       ANNEXURES 
       [0150]    Annexure 1 is a paper entitled “Validation of ASX Property Indices” which provides an estimate of the accuracy of daily indices with monthly indices used hitherto, and Annexure 2 is a paper entitled “Calculating High Frequency Australian Residential Property Price Indices” which explains the temporary dropping or removal of a property from the index where there is a value change of the nature of a capital inflow (eg an additional bedroom) or of the nature of a capital outflow (eg a lessening of the land size as a result of a sub-division) so that the index is correctly self-financing. 
       INDUSTRIAL APPLICATION 
       [0151]    The methods and processes described above are preferably practised using a conventional general-purpose computer system  60 , such as that shown  FIG. 6  wherein the processes are implemented as software, such as an application program executed within the computer system  60 . In particular, the steps of the processes are effected by instructions in the software that are carried out by the computer. The software can be divided into two separate parts; one part for carrying out the specific processes; and another part to manage the user interface between the latter and the user. The software is able to be stored in a computer readable medium, including the storage devices described below, for example. The software is loaded into the computer from the computer readable medium, and then executed by the computer. A computer readable medium having such software or computer program recorded on it is a computer program product. The use of the computer program product in the computer results in an advantageous apparatus for carrying out embodiments of the invention. 
         [0152]    The computer system  60  comprises a computer module  61 , input devices such as a keyboard  62  and mouse  63 , output devices including a printer  65  and a display device  64 . A Modulator-Demodulator (Modem) transceiver  76  is used by the computer module  61  for communicating to and from a communications network  80 , for example connectable via a telephone line  81  or other functional medium. The modem  76  can be used to obtain access to the Internet, and other network systems, such as a Local Area Network (LAN) or a Wide Area Network (WAN) or other computers  160 ,  260 , . . .  960 , etc each with their own corresponding modem  176 ,  276 , . . .  976 , etc and each having a data input terminal  162 ,  262 , . . .  962 , etc. Each of the computers  160 - 960  are used to collect data for the preparation of an index, for example. 
         [0153]    The computer module  61  typically includes at least one processor unit  65 , a memory unit  66 , for example formed from semiconductor random access memory (RAM) and read only memory (ROM). There are input/output (I/O) interfaces including a video interface  67 , and an I/O interface  73  for the keyboard  62 , mouse  63  and optionally a card reader  59 , and a further interface  68  for the printer  65  or optionally a camera  77 . A storage device  69  is provided and typically includes a hard disk drive  70  and a floppy disk drive  71 . A magnetic tape drive (not illustrated) can also be used. A CD-ROM drive  72  is typically provided as a non-volatile source of data. The components  65  to  73  of the computer module  61 , typically communicate via an interconnected bus  64  and in a manner which results in a conventional mode of operation of the computer system  60  known to those in the relevant art. Examples of computers on which the embodiments can be practiced include IBM-PC&#39;s and compatibles, Sun Sparcstations or alike computer systems evolved therefrom. 
         [0154]    Typically, the application program of the preferred embodiment is resident on the hard disk drive  70  and read and controlled in its execution by the processor  65 . Intermediate storage of the program and any data from the network  80  is accomplished using the semiconductor memory  66 , possibly in concert with the hard disk drive  70 . In some instances, the application program is encoded on a CD-ROM or floppy disk and read via the corresponding drive  72  or  71 , or alternatively is read from the network  80  via the modem device  76 . Still further, the software can also be loaded into the computer system  60  from other computer readable media including magnetic tape, a ROM or integrated circuit, a magneto-optical disk, a radio or infra-red transmission channel between the computer module  61  and another device, a computer readable card such as a PCMCIA card, and the Internet and Intranets including email transmissions and information recorded on websites and the like. The foregoing is merely exemplary of relevant computer readable media. Other computer readable media may be practiced without departing from the scope and spirit of the invention. 
         [0155]    It should not be lost sight of that the purpose of the computer system  60  is to generate a digitally encoded electric signal (such as that illustrated in  FIG. 7 ) which when applied to an output interface (such as the display device  64  or the printer  65 ) produces an indicium or indicia which convey information and which are legible or intelligible to a human. For example, the electric signal illustrated in  FIG. 7  is a binary encoded signal 00101 which when applied to the display device  64  or printer  65  causes the indicium  9  to be displayed or printed. 
         [0156]    The processes can alternatively be implemented in dedicated hardware such as one or more integrated circuits performing the functions or sub functions of the processes. Such dedicated hardware can include graphic processors, digital signal processors, or one or more microprocessors and associated memories. 
         [0157]    The foregoing describes only some embodiments of the present invention and modifications, obvious to those skilled in the financial and computing arts, can be made thereto without departing from the scope of the present invention. 
         [0158]    The term “comprising” (and its grammatical variations) as used herein is used in the inclusive sense of “including” or “having” and not in the exclusive sense of “consisting only of”.