Land Registry House Price Index Methodology. Discussion and Description of Methodology

Transcription

1 Land Regisry House Price Index Mehodology Discussion and Descripion of Mehodology 1

2 Table of Conens Table of Conens... 2 Background... 3 Inroducion... 4 The Logic of Compiling a House Price Index... 5 Repea Sales Regression Mehodology... 7 The Model Seing... 7 Model Esimaion and Resuls Analysis... 9 Feaures of Repea Sale Regression Mehod Revision Seasonal Adjusmen References

3 Background Calnea Analyics believes ha he new Land Regisry House Price Indices represen a significan advance in he measuremen of house price changes hroughou he counry boh in erms of naional reliabiliy and local coverage. Unlike oher house price saisics produced by various insiuions, he Land Regisry House Price Index is based upon he Repea Sales Regression (RSR) Mehod. Under he RSR mehod, house price growh is measured by looking a houses which have been bough or sold more han once. The use of repea ransacions conrols for differences in he qualiy of he houses comprising he monhly sample hereby a more consan qualiy comparison. The Index is produced from he Land Regisry s own price paid daase ha conains he ransacion deails of every residenial propery sold in England & Wales. Calnea Analyics has creaed for he Land Regisry a series of local and naional indices ha provide a picure of wha has happened o house prices across he counry. 3

4 Inroducion There is a very srong ineres in house price movemen among various media, public and financial secors hroughou England & Wales. The measuremen of house price movemens is by no means an easy ask; he primary complicaion is because houses are heerogeneous goods. No wo houses are exacly he same, and even seemingly idenical houses wihin he same small geographic area will follow differen (hough ofen correlaed) processes of price appreciaion. Therefore, i is convenien and useful o capure he overall average price rends followed by a group of houses. In such a siuaion, i is common pracice in economics o propose a single price index, e.g. consumer price index, ec. A house price index is simply one of many plausible measures of he cenral endency of house price appreciaion for a paricular group of properies (Araham and Schauman, 1991). Accurae and reliable house price informaion is demanded for a range of applicaions, from economic and social policy o he decisions of privae invesors. Informaion on house price levels and growh raes form he basis of key decisions made by a wide variey of privae acors for example; home buyers, home sellers, morgage lenders, valuaion surveyors, and house builders. An accurae house price index would inform naional economic and social policy by providing a key indicaor of housing marke and wider macro-economic condiions. A measure of house prices is also needed for research ino he sources of inflaion, economic growh and cycles. House price appreciaion can also be an indicaor of he srengh of he local economy and differences in appreciaion raes can reflec differences in regional economic performance. In addiion here is a naural ineres in house prices amongs he growing home-owning populaion; housing represens he mos valuable asse for many people. Accurae price series on a large number of asses, such as equiies and bonds, are an essenial feaure of financial marke research. Analogous informaion for local propery markes (e.g. relaively small ciies and owns) would be useful no only o researchers bu also o own officials and homeowners. 4

5 The Logic of Compiling a House Price Index 1 The key daase one normally has for compiling a house price index is a se of price observaions, eiher acual ransacion prices when a house is sold, or assessed prices according o professional opinion (Clapp and Giaccoo (1992) consider assessed price as useful daa o esimae house price index). Wih abundan price daa a various ime periods available for he group of properies in a paricular housing marke, house price indices can be esimaed for using saisical echniques. Idealized Daase Ideally, wih perfec informaion we would be able o observe every house s price a every poin in ime. If we denoe he price of house n a ime as P n,, and assume ha he populaion size of properies is N and he oal number of periods under consideraion ist +1, he perfec informaion daase will come in a marix form, which is illusraed in Table 1. Also, denoe he underlying unknown index of period as I. Our goal is o esimae he whole series of I for 0,..., T from he marix of daa se of P n,. Table 1: An ideal daase of house prices TIME T Propery 1 P 1,0 P 1,1 P 1,2 P 1,3 P 1, P 1,T Propery 2 P 2,0 P 2,1 P 2,2 P 2,3 P 2, P 2,T Propery 3 P 3,0 P 3,1 P 3,2 P 3,3 P 3, P 3,T Propery 4 P 4,0 P 4,1 P 4,2 P 4,3 P 4, P 4,T Propery n P n,0 P n,1 P n,2 P n,3 P n, P n,t Propery N P N,0 P N,1 P N,2 P N,3 P N, P N,T Indices I 0 I 1 I 2 I 3 I I T-1 1 Par of his secion is adoped from Wang and Zorn (1997). 5

6 Real Daase However, in realiy, house prices are no readily observable coninuously. The price of any given propery is observable only when i is sold or i is assessed by a professional assessor, boh of which happen a infrequen inervals. In addiion, new houses are always being consruced and old houses demolished (Wang and Zorn, 1997). Accordingly, he populaion of houses we can observe is changing over ime. Consequenly, raher han having he convenien daa marix illusraed in Table 1, we have insead a fragmened daase as shown in Table 2. This fragmened daase is he saring poin of any index esimaion. Table 2: Daase of House Prices in Realiy TIME T Propery 1 P 1,1 P 1,5 Propery 2 P 2,0 P 2,6 Propery 3 P 3,2 P 3,4 Propery 4 P 4,3 P 4,7 Propery 5 P 5,2 P 5,5 P 5,T Propery 6 P 6,1 P 6,3 Propery 7 P 7,2 P 7,6 Propery 8 P 8,8 P 8,T Propery 9 P 9,5 P 9,6 P 9,8 Propery 10 P 10,2 Indices I 0 I 1 I 2 I 3 I 4 I 5 I 6 I 7 I 8 I T The lesson from he above illusraion is ha he qualiy of available daase (size, accuracy, upo-dae, ec.) is he bigges facor in house price index esimaion. The wo main, heoreically rigorous echniques used o esimae house price indices are hedonic regression and repea sale regression. Due o hisoric daa unavailabiliy, he more suiable repea sale regression mehod was no used in he UK. Now, wih he wealh of daa on individual ransacion prices available o he Land Regisry i has become possible o implemen his more suiable mehod. 6

7 Repea Sales Regression Mehodology Repea sales regression mehod (RSR, hereafer) of esimaing house price indices was firs inroduced by Bailey, Muh and Nourse (1963). The main idea behind RSR is ha marke-wide growh rae for a given period is refleced in averaging he observed individual growh raes of all properies ha were ransaced wice in ha ime period (Leishman, 2000). Wherever he necessary daa has been available, RSR has gained populariy in economic applicaion. RSR is now widely adoped by a number of large privae, sae and federal organizaions in he Unied Saes, for example he highly influenial Housing Economics and Financial Research Deparmen a Freddie Mac (Federal Home Loan Morgage Corporaion). RSR is also a crucial ool widely used in he sudy of oher markes characerized by infrequen rading, such as he ar marke (Goezmann, 1992). The Model Seing The model underlying Bailey e al (1963). mehod can be wrien as follows, using he noaion we inroduced above. Any propery n ha has been sold wice saisfies he following equaion, R P I, n, 2 2 n, 1, U 2 n, 1, 2 Pn, I 1 1 where U is an idiosyncraic error erm; 1 2, for 1 0,1,..., T 1, 2 1,..., T n, 1, 2 The model means ha he raio of he final sales price in period 2 o iniial sales price in period 2 for he n h propery, which is defined as R n, 1,, is equal o he raio of he (unknown) 2 indices of he corresponding wo periods wih a propery-specific noise erm. The inuiion behind he model is obvious. A pair of sales prices of a given propery conains informaion on house price appreciaion happening in he marke i belongs o wihin he periods beween he firs and second sale (Case and Shiller, 1987). Therefore, he observed price appreciaion beween he wo sales of his given propery can be aribued o wo facors: 1) he general rend of appreciaion of he housing marke his propery belongs o, and 2) some propery-specific elemens ha drive is house price o deviae slighly from he overall rend of 7

8 he housing marke. The firs facor is represened in he index raio I / I 2, while he second 1 facor is capured by he error erm U n, 1,, in he above model. 2 To make he BMN model more pracical, one can ransform he model ino a linear form by aking logarihm of boh sides of he equaion, log( R ) log( I ) log( I ) log( U ), or n, 1, n, 1, 2 r i i u n, 1, n, 1, 2 where lower case leers sand for he logarihms of he corresponding capial leers. In BMN model, i is assumed ha he error erm u have zero means, consan variance, and n, 1, 2 are uncorrelaed wih each oher and any i. Recall he goal is o esimae I, or, equivalenly, i, for 0,..., T. If one makes T dummy variables x, for 0,..., T, and rewries he model above as, T 1 r i b u n, 1, 2 n, 1, 2 0 where, if we denoe iniial sale and final sale periods as 1 and 2 respecively, 1, if 1 x 1, if 2 0, oherwise Therefore, he model becomes muliple linear regression model wih T dummy independen variable. In marix noaion, he model is: r X i u, M 1 M T T 1 M 1 if X is he daa marix of he dummy variables x, and here are alogeher M pairs of repea sales prices in he sample. 8

9 Model Esimaion and Resuls Analysis The model can be esimaed by ordinary leas squared (OLS) mehod, which can be implemened by various saisical packages. The regression oupu are he esimaed parameers i, for 0,..., T, which in marix form is 1 i ( X ' X ) X ' r However, his is sill in logarihm form. In addiion, i is convenion o have an index of 100 for base period 0, i.e., I If we denoe he indices esimaed from OLS as I, and indices afer rebasing as I, for 0,..., T, he rebase relaionship is he following: I I 0 I, I 0 which is equivalen o, Therefore, for 0,..., T, log( I ) log( I ) log( I ) log( I ) i i log(100), I exp( i i log100), 0 We hence ge house price indices I, for 0,..., T, afer rebasing I

10 Feaures of Repea Sale Regression Mehod Separaing Qualiy from Price The greaes srengh of RSR is is mehod of separaing qualiy from price (Araham and Schauman, 1991). The difficuly hedonic mehod facing is he fac ha i relies heavily on he correc specificaion of boh he funcional form of he model and he se of propery characerisics (Meese and Wallace, 1997). Case and Quigley(1991) illusraed hedonic model in a general form, P f ( x, ), i.e., house price is a funcion of ime and he vecor of all physical and locaional characerisics x. This requires f o be correcly specified, and he vecor x is correcly chosen and accuraely measured, none of which can be guaraneed. These can poenially inroduce wha is known as misspecificaion bias (Bailey, Muh and Nourse, 1963; Case and Shiller, 1987). For he RSR mehod, researchers conrol for hedonic characerisics by examining only hose properies ha have been sold more han once during he period under consideraion. Case and P / P ( Quigley(1991) provided a general expression of RSR model as: 1 g 2 1, 2), which obviously highlighs he feaure of RSR ha i only depends on price daa and ransacion daes, boh of which can be measured accuraely. The funcional form g is also unique in heory, which is a clear advanage over hedonic regression. Less Sric Daa Requiremens RSR s less sric daa requiremen is advanageous. As menioned above, while hedonic mehod is a valid echnique, is onerous daa requiremens limi he daases ha can be used. Daa on many of he aribues ha can be imporan deerminans of he price of a propery, paricularly qualiaive aribues such as neighbourhood, locaion, qualiy of workmanship, ec., are ofen no available (Case, Pollakowski and Wacher, 1991). Usually, large daabases conaining informaion on boh quaniaive and qualiaive aribues are buil by morgage lenders. Using such daabases can inroduce wo poenial sources of bias: a) he sample includes only hose loans and properies ha are subjec o hisorical agency purchase paerns and loan amoun resricions ; b) he inclusion of refinancing ransacions whose propery values represen esimaes by appraisers can bias a 10

11 derived price index (Cho, 1996). The ype and locaion of properies over which hese insiuions gran morgages are no randomly disribued. The daa available o he lenders are from heir respecive cusomers bases concenraed in he Norh and Souh of he counry respecively. RSR requires only daa on ransacion prices and daes of wo consequen ransacions, and does no require daa on physical aribues. Thus, RSR is able o capure he informaion value from much larger naionwide daases. In addiion, RSR enables he creaion of local house price indices. This is due o he fac ha he daa requiremens of RSR are grealy simplified when compared wih hedonic regression mehods. This simpliciy gives RSR grea prospec of applicaion and significan poenial for applicaion o local housing markes. UK based academics have proposed he applicaion of RSR in developing a sysem of local house price indices (Cosello and Wakins, 2002) (Leishman Wakins and Fraser, 2003). As frequenly cied, one of he main problems wih he exising indices is heir lack of geographic focus. Munro & Maclennan (1986) poin o he need o examine house price appreciaion raes a neighbourhood level and cauion agains making assumpions abou he aggregae naure and behaviour of markes. Daase As Araham and Schauman (1991) poined ou, he bigges obsacle in creaing a repea-sales index is geing he necessary large amoun of raw daa. The fac ha he RSR mehod is no in use in he UK ye (Fleming and Nellis, 1994) is due o he limied availabiliy of daa on a large scale. Leishman (2000), Leishman and Wakins (2002), and Leishman, Wakins and Fraser (2002) have done some pioneering research in applying RSR mehod in UK. Bu due o he limied availabiliy of daa, heir work was confined o experimening wih RSR using daa from a number of ciies in Scoland. The problem wih respec o daase resricion was eased by one significan facor - he resoraion of price paid ino he Land Regiser in England and Wales from April 1 s, Land Regisry has uilised ouside economics and valuaion experise o creae a whole sysem of naional and regional house price indices, and has launched he is sysem of indices using he RSR mehod in he UK. 11

12 This secion below focuses on he Land Regisry daabase and he sampling echnique used by Calnea Analyics. Size The daabase used is he Land Regisry price paid daase. Since every residenial propery ransacion in England & Wales is required o file wih he Land Regisry, he Land Regisry daabase conains every open-marke propery ransacion since January 1 s, Beween April 1s, 2000 and April 30, 2006 approximaely ~7,000,000 ransacion records were available for analysis. This figure increases by approximaely 100,000 records per monh. The Land Regisry daabase is he larges and mos comprehensive daabase of house selling prices available for England & Wales. I is clearly a major advanage o have he larges daabase, as for a given level of aggregaion, more daa means igher sandard errors abou he esimaed mean of he price series (Araham and Schauman, 1991). The large size of he daa also gives us he abiliy o develop a sysem of indices for finer geographic and price bands han any oher index. Recen House prices ake beween 1 and 3 monhs from he dae of sale before being regisered wih he Land Regisry. The Land Regisry provides Calnea Analyics wih monhly updaes for all ransacions occurring a monh prior o he dae. Cleansing A daa cleansing process is necessary in order o spo poenial coding errors and ouliers in he daabase. Conservaive ediing sandards exclude all ransacions where doubs over validiy exis. Similar o he RSR pracices employed by US insiuions; boh Land Regisry and Calnea Analyics employ a cleansing process o filer address errors and various oher ouliers. House Improvemen Adjusmen RSR requires ha he propery has undergone neiher a significan enhancemen in value, such as remodeling, nor subsanial physical deerioraion (Araham and Schauman, 1991), so ha he single propery price appreciaion can be aribued solely o rends of marke price movemen. I is clear ha all properies experience physical depreciaion and in addiion many properies are improved prior o sale. 12

13 There are wo conrasing approaches in he academic lieraure. One approach advocaes no adjusing for his issue. I could be poenially argued ha overime in he long-run he value of house improvemens will equae o he value of depreciaion such ha his facor will hold consan. If i is viewed ha he main componen of value is space, i.e. square fooage hen he argumen regarding he irrelevance of depreciaion or improvemens gains srengh. The alernaive approach is o make an adjusmen o he index o reflec he average value of improvemens minus depreciaion. Araham and Schauman (1991) believe ha i is possible o correc he index direcly from knowledge of he value of improvemens naionwide. A perfec RSR model would require he absence of sysemaic propery deerioraion or improvemen across he sample. I is worh noing ha his same bias affecs hedonic model varians in so far as he home improvemen or deerioraion is no perfecly capured by he hedonic variables. I is he view of Calnea Analyics ha in so far as daa on sysemaic propery deerioraion or improvemen is available, his informaion can and should be pracically incorporaed in he model. One valuable source of such informaion is he English House Condiion Survey (EHCS) annually conduced by he Office of he Depuy Prime Miniser. Technical Consideraions There are a number of echnical issues ha arise from implemenaion of he RSR mehod. I mus be recognized ha no index esimaion mehod is perfec, and he RSR mehod, while we believe o be exremely robus and value adding, is no by naure bias free. A descripion of various echnical issues follows below. Heeroskedasiciy u Recall in he model of Bailey, Muh and Nourse (1963), n, 1, 2 is by convenion assumed o have u consan variances. Case and Shiller(1987, 1989) argued ha n, 1, 2 his is called heeroskedasiciy in economeric lieraure. has varying variances, and The soluion Case and Shiller(1987, 1989) provided was a weighed repea sale model. However, i has been shown ha he effec of he model is ambiguous. Leishman and Wakins (2002) using Scoish daa, applied boh he normal RS mehod and weighed RS mehod and concluded ha he normal RS mehod was preferred. 13

14 Sample selecion An imporan feaure of he RSR mehod is ha he sample used in RS regression only includes houses ha have been sold more han once, and herefore suffers from sample selecion bias (Case, Pollakowski and Wacher, 1991; Cho, 1996; Gazlaff and Haurin, 1997; Meese and Wallace, 1997; Seele and Goy, 1997). The empirical sudy of Clapp, Giacoo and Tiriroglu (1991) found no sysemaic differences beween he RS sample and he full sample of all ransacions over he long run. They argued ha arbirage ypically forces prices for he repea sample o grow a he same rae as hose for he full sample. Anoher sudy of Wallace and Meese (1997) also arrived a he conclusion ha he sub-sample of RSR is acually represenaive of all home sales during he period under consideraion. The RSR index is naurally more reflecive of properies ha ransac more frequenly. In so far as a differenial in price appreciaion exiss beween properies based on he relaive frequency of ransacions, he RSR measure will be naurally weighed owards he more frequenly ransacing subse of properies. There are a variey of reasons why he holding duraion of properies migh be unevenly disribued. The increase in ransacion coss for more expensive properies due o samp duy may resul in a decreased urnover of more expensive homes. Life-cycle heories on propery holding period posi ha less expensive properies are raded more frequenly - when people move up he propery ladder hey end o move home less ofen. In addiion he Buy-o-Le marke is more acive in he lower price brackes. Policy-makers need o be aware of he price appreciaion differenials beween submarkes, especially when here is sysemaic variaion in he frequency of ransacions beween hese submarkes. The char ha follows illusraes differenials in price appreciaion according o price bracke. 14

15 Index Appreciaion by Price Band Apr-00 Apr-01 Apr-02 Apr-03 Apr-04 Apr-05 Apr-06 Inconsancy of aribue appreciaion If one akes a hedonic perspecive o consider a house as a bundle of separae aribues, boh qualiaive ones and quaniaive ones, he seing of RS mehod implicily assumes ha he prices of all hese aribues move a he same rae overime, which may no be he case (Case, Pollakowski and Wacher, 1991). However negligible his is anoher poenial bias of house price indices. Mulicollineariy Mulicollineariy refers o siuaions where here is an approximae linear relaionship among independen variables (Kennedy, 2003). This is no a rare phenomenon in economerics. Alhough he Gauss-Markov Theorem sill ensures a bes linear unbiased esimaor, some problems can be caused in applied research. 1. Small changes in he daa produce wide swings in he parameer esimaes. 2. Coefficiens may have very high sandard errors and low significance levels even hough hey are joinly significan and he 2 R for he regression is quie high. 3. Coefficiens may have he wrong sign or implausible magniudes. (William, 2003) 15

16 Unlike oher model specificaion problems, he problem of mulicollineariy is caused by he specific sample used in he regression (Kennedy, 2003). Cho (1996) poined ou ha his problem ends o arise wih small sample sizes. Wih only a small percenage of ransacions, wo columns of he daa marix are by consrucion similar and hence highly correlaed. However, his problem does no presen iself maerially in he UK housing marke. The housing marke liquidiy in England & Wales is greaer han mos oher counries. Wih ~100,000 residenial propery ransacions per monh and close o a 70% rae of home-ownership, he naional sample daa does no suffer from maerial mulicollineariy. Inefficiency One complain abou he RSR mehod is ha i only uses a porion of he ransacion daase (i.e. i only uses mached pairs and ignores oher ransacions) herefore suffering from inefficiency. Such commens ofen fail o noe ha he explanaory and informaive power of he remaining porion of daa (i.e. he mached pairs) is noably superior o a similar sized daase used by hedonic mehods. A RSR index based on 100 price observaions is superior o a Hedonic index based on 100 price observaions ceeris paribus. The reason for his is ha he hedonic mehods also suffer from informaion inefficiency. There is an imporan disincion is beween an index uilising 100% of ransacions versus an index uilising 100% of he possible informaion. Model improvemen Some academic lieraure has been devoed owards proposing hybrid models, which end o combine he meris of boh hedonic mehod and repea sale mehod (Meese and Wallace, 1997; Case, Pollakowski and Wacher, 1991). Alhough a heoreical aracive idea, he hybrid models sill suffer from he model specificaion and variable measuremen problems of normal hedonic models. Empirical sudies have yielded ambiguous resuls, and he superioriy of hybrid models o RSR models a leas a he momen lacks of supporive evidence. Clapp and Giaccoo (1992) ried o use an assessed value mehod o improve he efficiency of he model, bu found he mehod gave subsanially he same esimaes of price rends as RSR gave over he long run. 16

17 Revision Revision of Hisorical RSR Indices Hisorical published daa is revised for wo main reasons: 1. Daa capure lag - here is a ime-lag beween he sale of a propery and he subsequen regisraion of his informaion wih Land Regisry. This means he ransacion daa for he previous monh will no be 100% complee when he monhly repor is prepared. The missing daa is included as i becomes available. 2. HPI based on repea ransacions - When Land Regisry publishes is HPI repors each monh, he index represens he view on hisoric house price movemens a he ime. As new informaion becomes available, he published indices are revised o reflec any new daa. Clapp and Giaccoo (1999) poined ou ha any RSR price index will be revised every ime new informaion becomes available. This is a feaure of any price index ha is in coninual receip of addiional informaion. As such his is classed as a pracical issue raher han a mehodological problem. Levels of undersanding in all fields are revised in he presence of addiional informaion. Due o he naure of saisical sampling, mos of he imporan indices and oher economic indicaors are subjec o revision (Baumohl, 2005). Shiller (1994) in his book argues ha revisions are a necessary aspec of any price index, wheher he index is based on a repeasales or hedonic model. Revision Size Whils revisions are o be expeced, policy-makers should be made aware ha revisions are relaively small. The following chars show a curren price index alongside one creaed in he previous monh as well as one creaed wih daa limied o hree years prior. All price indices shown are prior o seasonal adjusmen. I is apparen ha he degree of revision does no represen a fundamenal change in esimaes. The Halifax HPI is displayed alongside he revisions in he second char in order o show how minimal revisions are in relaion o he volailiy of anoher index. 17

18 Index Revisions - Monhly Change 6% 4% 2% 0% -2% -4% curren previous monh 3 years ago -6% May-2000 May-2001 May-2002 May-2003 May-2004 May-2005 Index Revisions - In Perspecive 6% 4% 2% 0% -2% -4% curren previous monh 3 years ago Halifax (NSA) -6% May-2000 May-2001 May-2002 May-2003 May-2004 May

19 RSR Index Revision 3 years ago previous monh curren Halifax Index Change Revision Index Change Revision Index Change Index Change Apr May % 0.0% % 0.0% % % Jun % -0.1% % 0.0% % % Jul % 0.2% % 0.0% % % Aug % -0.1% % 0.0% % % Sep % -0.4% % 0.0% % % Oc % -0.2% % 0.0% % % Nov % 0.0% % 0.0% % % Dec % 0.4% % 0.0% % % Jan % 0.2% % 0.0% % % Feb % -0.3% % -0.1% % % Mar % -0.3% % 0.0% % % Apr % 0.3% % 0.0% % % May % 0.2% % 0.0% % % Jun % 0.3% % 0.0% % % Jul % -0.5% % 0.0% % % Aug % 0.1% % 0.0% % % Sep % -0.3% % 0.0% % % Oc % -0.2% % 0.0% % % Nov % -0.4% % -0.1% % % Dec % 0.5% % 0.0% % % Jan % -0.8% % 0.0% % % Feb % -0.2% % 0.0% % % Mar % 0.5% % 0.0% % % Apr % 0.5% % 0.0% % % May % -0.1% % 0.0% % % Jun % 0.4% % 0.0% % % Jul % 0.0% % 0.0% % % Aug % -0.2% % -0.1% % % Sep % 0.1% % 0.0% % % Oc % -0.4% % 0.0% % % Nov % -0.1% % 0.0% % % Dec % 0.2% % 0.0% % % Jan % -0.2% % -0.1% % % Feb % -0.2% % 0.1% % % Mar % -0.5% % 0.0% % % Apr % 0.0% % 0.0% % % May % 0.0% % % Jun % 0.0% % % Jul % 0.0% % % Aug % 0.0% % % Sep % 0.0% % % Oc % 0.0% % % Nov % 0.0% % % Dec % 0.0% % % Jan % -0.2% % % Feb % 0.0% % % Mar % 0.0% % % Apr % 0.0% % % May % 0.0% % % Jun % 0.0% % % Jul % 0.0% % % Aug % 0.0% % % Sep % 0.0% % % Oc % 0.0% % % Nov % 0.0% % % Dec % 0.0% % % Jan % -0.6% % % Feb % -0.1% % % Mar % 0.0% % % Apr % 0.0% % % May % 0.0% % % Jun % -0.1% % % Jul % 0.1% % % Aug % 0.0% % % Sep % 0.0% % % Oc % 0.0% % % Nov % 0.0% % % Dec % 0.2% % % Jan % -0.1% % % Feb % 0.0% % % 19

20 Seasonal Adjusmen Seasonaliy in ime series The seasonal componen in ime series corresponds o he regular movemens observed in monhly ime series during a welve-monh period. The rae of house price inflaion and propery marke ransacion volumes are examples of his. Oher examples include increases in reail sales daa associaed wih he Chrismas period or he fall in indusrial aciviy during holiday periods. Why publish seasonally adjused daa Presening a ime series from which he seasonal movemens have been eliminaed allows he comparison of daa beween wo monhs for which he seasonal paern is differen. Also seasonal effecs on non-adjused or original daa make i difficul o made valid comparisons over ime using hese daa, paricularly for he mos recen period. Consequenly, seasonally adjused daa are always used in economic modelling and cyclical analysis. Presenaion of daa on a seasonally adjused basis allows he comparison of he evoluion of differen series which have differen seasonal paerns and is paricularly perinen in he conex of house price index comparisons since asking price, morgage approval price indices and acual selling price indices may be in differen seasons a idenical periods of he year. In daa analysed by Calnea Analyics boh raw and seasonally adjused figures are shown for a large number of indicaors. Adjusmens for seasonal variaion Seasonal adjusmen is calculaed using Classical Seasonal Decomposiion (Census Mehod 1) oherwise known as he raio-o-moving-average mehod. The purpose of he seasonal decomposiion mehod is o isolae hose componens, ha is, o de-compose he series ino he rend effec, seasonal effecs, and remaining variabiliy. For house price indices and ransacion volume analysis we adop he muliplicaive model as he ampliude of he seasonal variaion is proporional o he level of he series. Seasonal adjusmen facors are recalculaed on a monhly basis. Those who wish o employ heir own adjusmen sofware (e.g. X-12-ARIMA, X11, TRAMO-SEATS ec.) are able o use he non adjused daa provided. 20

21 Seasonal Facors (%) May Jan Feb Mar Apr May Jun Jul Aug Sep Oc Nov Dec Seasonal Facor

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