Development of Low Water Stage Impact Relationships for Identified Study Sites:

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1 Develpment f Lw Water Stage Impact Relatinships fr Identified Study Sites: Reprt t the Castal Zne Technical Wrking Grup n Task 4 Preparatin f strategy fr develpment f hednic pricing mdel Prepared by Mark Dunning and Dnielle Jrdan, CDM 1 Revised Nvember 30, 2010 Purpse Hednic pricing mdels (HPM) can be used t estimate the cntributin f amenities such as waterfrnt lcatin t market prices fr real estate. They can als be used t explre hw factrs such as changes in lake levels may impact real estate prices. The Castal Zne Technical Wrking Grup (CZ TWG) has specified in Task 4 2 that an assessment f the feasibility and apprach fr develping an HPM be made. This reprt addresses the fllwing tpics: Overview f HPM Cnstructing an HPM Data cllectin Preferred sites HPM csts and benefits versus alternative appraches Mdel framewrk and peratin 1 Tim Feather f CDM had a significant rle in the review f the draft reprt as it was being develped. 2 The cntractr will develp a brief utline f the strategy fr the ptential develpment f hednic pricing mdels at the defined study sites. The utline shuld identify required data including that determined in the fcus grups, ptins fr data surces, alng with csts t btain the data (if purchasing is necessary). Within the strategy, the cntractr will utline the prs and cns f undertaking such an analysis given the utcmes f Tasks 2 and 3 abve. Castal TWG and Plan Frmulatin and Evaluatin Grup representatives shuld be included in discussins f mdeling strategies t ensure the expectatins f mdel peratinal requirements are incrprated. Such expectatins include the use f pen surce cding and if pssible, the ability t perate the mdel within a spreadsheet applicatin such as MS Excel. The cntractr will als prpse tw (2) preferred sites frm the suite f ptential sites t test the mdeling apprach (ne Canadian and ne U.S.). 1

2 HPM develpment schedule Overview f Hednic Pricing Mdels The hednic price methd discerns the value f ne attribute f a multi attribute gd r service thrugh statistical analysis f market transactins (see Yung 2005, p. 330). In the case f using HPM t identify factrs cntributing t real estate prices Feather (2008, p.5) ntes: the price f a residential prperty reflects the characteristics f that prperty lcatin, size, type f cnstructin, neighbrhd, amenities, etc. These characteristic cmpnents are part f the asset price f the husing bundle. T reveal the implicit hednic price, ecnmetric techniques unbundle these cmpnents. Thrugh use f multiple regressin analysis, the respective cntributins f these cmpnents t the ttal market values f prperties in a ppulatin can be inferred and statistically verified. Predictins made regarding current and future prperty value effects, if any, resulting frm changes in the cmpnents, can be derived. The general frm f the HPM specificatin is: P i = f(s i, N i, Q i ) Where P i is the price f prperty i, S i are the structural characteristics f prperty i, N i are the neighbrhd characteristics f i, and Q i are the envirnmental quality characteristics f prperty i (Freeman 1993). Cnstructing an HPM Research Questins the Mdel Shuld Explre The cnstructin f an HPM is undertaken t address questins f the influence f varius characteristics r factrs n the price f a gd. In this case, the basic research questin the HPM wuld explre is What is the relatinship between lake level changes and prperty values? The literature reviewed fr the preparatin f the benefits transfer paper (Dunning and Jrdan 2010a) suggests that when lake water levels are identified as interfering with key water riented amenities a negative effect n residential prperty prices can be experienced. Interviews cnducted with lcal real estate prfessinals (Dunning and Jrdan 2010b) suggested that when impacts f lwer lake levels are visible and perceived t be severe such as grwth f shreline vegetatin and impedance f bating access price drps in waterfrnt real estate can ccur. The HPM wuld need t be structured t enable explratin f a number f refinements t the basic research questin such as (a) Must lake levels drp beynd certain levels befre husing prices are affected? (b) Must certain kinds f impacts assciated 2

3 with lwer water levels be present (e.g. presence f shreline vegetatin) befre husing prices are impacted? (c) Are nly waterfrnt prperties affected, r des the negative impact extend t waterview and ther prperties? Mdel Specificatin An HPM is nly as gd as the cmpleteness f variables cmprising the mdel 3. It is imperative t include thse factrs affecting real estate prices. This is particularly true when there have been verlapping cnditins which culd affect real estate prices such lwer lake levels and a cncurrent recessin and ecnmic dwnturn. T begin the prcess f identifying variables an examinatin f the key independent variables used in the HPM studies previusly reviewed and used in the Task 2 Benefits Transfer paper (Dunning and Jrdan 2010a) was perfrmed. Appendix 1 shws studies and the breakdwn f variables used rganized by the three categries (structural, neighbrhd, and envirnment). This review shws that the mst frequently appearing variables in these HPM were: Structural Variables: Prperty square ftage # f baths Lt size # garages Cnstructin quality Age f prperty Neighbrhd Variables: Cunty f lcatin Schl district f lcatin Envirnmental Factrs: 3 Failure t include imprtant variables in an explanatry mdel can result in Omitted Variable Bias (OVB) and can bias the parameters f regressin equatins. Since we are interested in determining the influence f lake levels n hme prices relative t ther ptential factrs it is imprtant t have cnfidence in the cefficients prduced in the regressin analysis. The nrmal appraches fr dealing with OVB are t first have a theretically sund basis fr the selectin f ptential explanatry variables based n an understanding and review f the literature, and secnd, t clsely examine the errr term prduced in the regressin equatin t ensure that it meets cnditins f nrmality (mean f 0, and cnstant variance). 3

4 Waterview Waterfrnt lcatin Feet f shreline Other Necessary Independent Variables Suggested by Research Questins: The variables listed belw were nt included in the majrity f the referenced studies; hwever, they wuld need t be addressed given the intent f the study and the research questins psed. Lake Level: Since the primary interest in a HPM will be determining the pssible influence f lake levels n husing prices it will be imprtant t include a lake level variable in the mdel. This variable culd be defined as the level f the lake expressed as its mean sea level (msl) value at the time f sale f the prperty. A variety f frmulatins f this variable culd be cnstructed and evaluated in the mdel t address varius hyptheses regarding the influence f lake levels n prices, e.g. creating a categrical variable t represent a threshld belw which negative impacts n bating and/r weedgrwth were mst likely t ccur. Shre Character: Interviews with real estate prfessinals as well as literature reviewed suggests that the greatest negative impacts f lw water levels are experienced n shres have gently slping relief. A dummy variable can be created such that a 1 indicates a shreline having gently slping relief (as revealed thrugh charts), while a 0 indicates a shre with a different character. Ecnmic Cnditins: It can be expected that recessinary cnditins exerted a pwerful and cnfunding influence n the prices f hmes. One readily available prxy measure fr lcal ecnmic cnditins in the United States is the Bureau f Labr Statistics reprted cunty unemplyment rate at the time f sale. Similar data is available fr purchase frm CANSIM ( debut eng.html) fr Ontari. Dependent and Classificatin Variables: Sales price: Since sales f prperties ccur ver an extended perid it is necessary t cnvert sales prices int cnstant dllars. Tls such as the Bureau f Labr Statistics inflatin calculatr ( bin/cpicalc.pl) can be used fr this purpse. Date f sale 4

5 Data Cllectin Data n prperty sales can be btained by accessing gvernment r quasi gvernment assessment ffices, and/r thrugh purchase f data frm prprietary data services, and/r generated by in huse analysis. It can be expected that a great deal f the time required t cmplete an HPM effrt will be cnsumed by the prcess f tracking dwn data, btaining it, and supplementing the data set with variables that need t be custm created. Table 1 shws the likely availability f data frm gvernment data bases based n a quick review f gvernment data sites, and the variables that wuld likely need t be custm created. Table 1. Availability f Data fr HPM Cnstructin Site Variable Saginaw Bay 4 Hlland 5 Duluth 6 Canadian 7 $ Sales Price Last Date f Sale Lake Level at Time f Sale Unemplyment Rate at Time f Sale Lt Size Prperty Square Feet # Garages cc cc cc Cnstructin Quality 4 Data btainable frm

6 Variable Saginaw Bay 4 Hlland 5 Duluth 6 Canadian 7 $ Site Age f Prperty at Time f Sale Cunty/ Twnship cc cc cc cc Schl District cc cc cc cc Water view cc cc cc cc Waterfrnt Lcatin cc cc cc cc Feet f Shreline cc cc cc cc = Infrmatin is btainable frm the gvernment assessr agency data surce listed +=Infrmatin is btainable frm anther gvernment surce $=Requires purchase (Market Mdel Reprts, $250/CN$ each reprt) CC=custm created variable Preferred Sites T be cnsidered as a ptential site fr applying an HPM the ideal site shuld meet a number f criteria: availability f gd data at reasnable cst, the site has experienced lake level changes, and thse lake level changes have created perceived impacts. Based n findings frm real estate interviews and evaluatin f data requirements, availability, and csts a US study site and a Canadian study site will be identified as recmmended sites fr applicatin f an HPM. Based n a preliminary assessment it appears that the Saginaw Bay and Windsr sites may ffer the best pprtunity t explre relatinships between negative impacts assciated with lake level changes and prices fr waterfrnt and waterview hmes. It is als pssible that Gergian Bay wuld als ffer a suitable pprtunity fr cnstructing an HPM fr a Canadian site since the islands have experienced negative impacts frm lw water levels related t access issues fr sme f the prperties lcated n a number f islands. This impact is different frm the vegetatin/vergrwth issues that mainland areas are experiencing. Using the Gergian Bay Islands may be an excellent way f investigating additinal impacts f lw water levels. 6

7 Alternatives t HPM An HPM study will require effrt fr data cllectin, data cleaning, and statistical analysis. Befre embarking n such a task it is useful t explre alternative appraches that might yield answers t the research questins in a less time cnsuming fashin. Tw alternative appraches are described and illustrated here: what is termed a simplified HPM apprach, and a time series analysis methd. Simplified HPM Apprach The simplified HPM apprach emplys the Zillw.cm website t select hmes that are n the market in U.S. study sites and creates an HPM like data base f variables cntained in the Zillw data. Fr this example, data analyzed included waterfrnt hmes alng Saginaw Bay, MI n Lake Hurn. In this area, data available n Zillw.cm primarily included hmes currently listed n the real estate market. The dataset cntained very few hmes previusly sld. As a result, the dependent variable in this case is asking price fr the prperty instead f actual sales price. Additinal infrmatin included square ftage, number f bedrms and bathrms, lt size, the date the prperty was listed, and the price per square ft. Where data was missing r unavailable (fr variables ther than price) the mean f the available data was substituted. This infrmatin, when cupled with infrmatin n lake levels and ther variables such as unemplyment rate, waterfrnt, waterview, and ther prperty identifiers can be used t perfrm a regressin analysis n asking price. Lake level during the mnth the hme was listed n the market (expressed in meters, and btained frm the Great Lakes Infrmatin Netwrk, at the Essexville, MI Statin). A binary (dummy) variable was created where a 1 signified a lake level that was at r higher than the lng term lake level average f meters, and a 0 indicated lake levels belw the lng term average levelunemplyment rate data was als added based n the year the hme was listed (data was taken frm the Bureau f Labr Statistics). Data was cllected fr 56 waterfrnt hmes lcated alng the shres f Saginaw Bay. Table 2 shws the results f an example simplified HPM using prperties in the study area. Table 2. Simplified HPM Apprach: Saginaw Bay Regressin Analysis Utilizing Zillw.cm Data Saginaw Bay Regressin Analysis Utilizing Zillw.cm Data Number f Observatins 56 Adj. R-Square Explanatry Variables Parameter Estimate Standard Errr t Value Pr> t Intercept Lake Binaries Lake Level Hme Characteristics 7

8 Square Ftage <.0001 Bath Lcatin Binaries Au Gres East Tawas Tawas City Results f regressin analysis shw that the prbability f significance is greatest between price listed and square ftage (strng significance is indicated by a Pr> t value f <.0001). Frm this relatinship, we can cnclude that the listing price f a waterfrnt hme n Saginaw Bay, Michigan is mst dependant n the square ftage f the hme. While the lcatin (city) and number f the baths have sme affect n the price f the hme, lake levels were fund t shw an inverse relatinship with asking price (i.e. lwer the lake level the higher the asking price); hwever the relatinship is nn significant (Pr> t.2895) 8. Unemplyment has been drpped frm this mdel as it was nnsignificant. Additinally, a Zillw search was perfrmed t identify Saginaw Bay area hmes currently n the market with Lake Hurn views r private lake access but which were nt physically lcated n the shreline. While these hmes are nt waterfrnt hmes, they wuld have clse private access t lake amenities. Nine hmes were identified alng the bay with these attributes. These were added t the database and indicated as nn waterfrnt hmes. A regressin analysis was cmpleted with this additinal data added. The variable fr waterfrnt vs. nn waterfrnt was added t the analysis. The results are displayed belw in Table 3. The equatin shws that hme asking prices are significantly crrelated with their lcatin n the water versus near the water. This result is cnsistent with findings reprted in the Benefits Transfer paper (Dunning and Jrdan 2010a). Lake level cntinues t display an inverse, albeit nn significant, relatinship t hme asking price. Table 3. Saginaw Bay Waterfrnt/Waterview Regressin Analysis Utilizing Zillw.cm Data Saginaw Bay Waterfrnt Regressin Analysis Utilizing Zillw.cm Data Number f Observatins 65 Adj. R-Square It shuld be stressed that this analysis is very preliminary and illustrative. Mre detailed analyses which is beynd the scpe f this paper wuld include in depth analysis f each variable s distributin and means, as well as crrelatin analyses, examinatin f cnfrmance f regressin equatin parameters t Ordinary Least Squares requirements, and examinatin f alternative frms f regressin equatins such as lg linear, and lg lg transfrmatins. Other mdels that might be explred include water level price relatinships at managed/inland lakes versus Great Lakes lcatins. 8

9 Explanatry Variables Parameter Estimate Standard Errr t Value Pr> t Intercept Lake Binary Lake Level Lcatin Binary Waterfrnt <.0001 Hme Characteristics Square Ftage <.0001 Bath Lcatin Binaries Au Gres East Tawas Tawas City Finally, in additin t regressin analyses, a T Test statistical analysis was perfrmed n this dataset t explre whether mean asking prices f hmes differed accrding t water levels. Mean asking prices f waterfrnt hmes listed when water levels were abve the lng term lake level average f meters were cmpared with mean asking prices when water levels were belw meters 9. The results f this analysis are shwn in Table 4. As can be seen the asking price fr hmes when lake levels were belw meters averaged $338,961 while the asking price fr hmes listed when lake levels were higher averaged $322,657. Hwever, the nrmal criteria fr a T Test is a.05 level f significance and the P(T<=t) value f 0.70 shwn in the table is greater than.05 and indicates that there is n significant difference between the asking prices in the tw categries. Table 4. T Test fr Tw Sample Waterfrnt Hme Listing Price T-Test: Tw sample Waterfrnt Hme Listing Price Lake Level <176.3 Lake Level > Mean $338,961 $322,657 Variance Observatins 49 7 Hypthesized Mean Difference 0 df 10 t Stat IGLD 1985 datum fr Lake Hurn is meters. All but ne lake level readings in the data set used fr these example analyses were abve the IGLD 1985 datum; the average lake level recrded fr the bserved data was

10 P(T<=t) tw-tail t Critical tw-tail Time Series Analysis Methd The time series analysis methd emplys the idea prpsed by Bill Werick in his mem f 3/31/10 suggesting that a time series lk at selling prices fr waterfrnt prperties in relatin t water levels in cmparisn t selling prices fr ther prperties in relatin t water levels might shed light n the presence f a pssible relatinship between water levels and husing prices fr waterfrnt hmes. Using Bill s ideas we cnstructed a Zillw.cm data base f three representative waterfrnt prperties in the Saginaw Bay study area and used the Zillw.cm ten year lk back feature t track estimated value f these hmes. These prices are then verlaid against mean mnthly lake levels taken frm the Essexville, MI statin. The verlay presented belw in Figure 1 represents the estimated hme values and lake levels frm January 2001 thrugh July The figure shws that there is little r n visible crrelatin in estimated hme values and Lake Hurn lake levels. While bth lake levels and hme prices experience perids f highs and lws, in des nt appear that these perids cincide with ne anther. Crrelatin cefficients fr each hme and lake levels were determined based n estimated hme values and lake levels. A strng crrelatin wuld be indicted by a value clse t 1. The crrelatin cefficient between the hme value n Killarney Beach Rad and Lake Hurn water levels was fund t be the lwest at The hme lcated n Linwd Beach Rad was fund t have the highest crrelatin with a cefficient f The hme lcated n Bay Shre Drive was fund t have a crrelatin cefficient f when cmparing estimated hme value with lake levels. These crrelatin cefficients average Based n the average crrelatin cefficient fr this dataset, we can cnclude that there is nt a strng relatinship between estimated hme values and lake levels fr this dataset. 10

11 Figure 1. Overlay f Saginaw Bay, Michigan Area Hmes with Lake Hurn Lake Levels HPM Csts and Benefits vs. Alternative Appraches Realtrs generally believe that lake level fluctuatins are built int real estate prices i.e. prices are nt affected by nrmal fluctuatins. Hwever, views prvided in Saginaw Bay and Windsr suggest that when impacts assciated with lw water levels becme disruptive e.g. interfere with bat access, result in lss f cntrl f ne s shreline, r entail csts t prperties (e.g. extending a dck) that such circumstances may negatively impact real estate prices. The quick lk at current prperties fr sale asking prices between 2008 and 2010 in the Saginaw Bay area failed t identify a relatinship between water levels and asking price. The visual inspectin and crrelatin analysis f the ten year lk back n sales prices f waterfrnt hmes in the Saginaw Bay area in relatin t lake levels similarly shwed n statistically significant difference in prices fr real estate. Given the lack f discernable relatinships between lake levels and real estate prices bserved with the tw alternative appraches emplyed it is likely that the HPM apprach wuld be necessary t fully 11

12 investigate the relatinship between lake level changes and husing prices 10. Hwever, given the evidence btained thus far, it may be premature t embark upn such a study until mre severe lake level changes manifest themselves. Mdel Framewrk It is quite prbable that an HPM wuld be cnstructed as a spreadsheet applicatin. Table 5 shws hw a spreadsheet mdel culd be cnfigured. It shuld be stressed hwever, that such a mdel wuld need custm tailring and tweaking t run n different sites. It wuld nt be a stand alne turnkey prduct. Table 5. Cntents f a Spreadsheet HPM Tab Cntents Data set Descriptive Statistics Regressin Equatin Spreadsheet rws represent cases f hme sales, clumns cntain variables Tables and graphs presenting means, standard deviatins, minimums, maximums f each variable Prviding cefficients, standard errrs, and measures f statistical significance f cefficients HPM Develpment Schedule Shuld a decisin be made t prceed with develpment f an HPM a likely schedule fr cmpletin fr tw sites is shwn in Table 6. Table 6. Schedule fr HPM Develpment 10 Based n the lack f relatinship fund thus far it wuld be very imprtant t include variables that identify shreline prperties negatively impacted by the results f lw lake levels (e.g. having shreline vegetatin, having restrictins n vegetatin remval, having dcks that are unusable, etc.). Develping this infrmatin may pse added difficulties. 12

13 Task Prduct Days 1. Mdel specificatin review Selectin f variables Data cllectin Obtain and create data sets at tw sites 30 (cincident with task 1) 3. Data cleaning and preliminary analysis Descriptive statistics and analysis f means Regressin analysis Regressin equatin, analysis f errrs 5. Presentatin f results Reprt n HPM analysis and results Ttal time requirement days 13

14 Appendix 1 Cmparisn f Variables Used in HPM Studies Referenced in the Benefits Transfer Paper Table 7. HPM Studies Reviewed in the Benefits Transfer Paper Study Regin Residential Prperty Values Fcus? Examine Lake Level Changes? Hednic Price Mdel Emplyed? Date f Study Publicatin Fcus f Study Bensn, et al Pacific NW (WA) Y N Y 1998 Value f views Bnd, et al GL (Lake Erie) Y N Y 2002 Value f lake views Kashian GL (WI) Y Y Y 2007 Reservir water level change impacts Lansfrd & Jnes Lmis & Feldman SW (T) Y Y Y 1995 Reservir water level change impacts Pacific SW (CA) Y Y Y 2003 Reservir water level change impacts Muller Feather MW (IN) & NE (CN) Mid Atlantic (Chesapeake Bay) Y Y Y 2007 Hednic mdel cnstructin; value f lake amenity factrs Y N Y 2008 Castal ersin susceptibility impacts n prperty values Table 8. Variables Used in Cnstructin f HPMs in HPM Studies Reviewed in Benefits Transfer Paper Studies Referenced in Benefits Transfer Paper Bensn, Bnd, et Kashian Lansfrd Muller et al. al and Jnes Variables Lmis and Feldman 2003 Feather 2008 Dependent Variables Sales price 14

15 Variables Bensn, et al Bnd, et al Studies Referenced in Benefits Transfer Paper Kashian 2007 Lansfrd and Jnes 1995 Lmis and Feldman 2003 Muller 2007 Feather 2008 Inflatinadjusted sales price Explanatry Variables Structural # Baths Building sq ft Lt size # Garages Street frntage Cnstructin quality Huse cnditin N. rms N. bedrms Central air Date f sale Age Acreage Remdeled Finished basement Deck Fireplace Rf style N. stries Mbile hme Grade f structure Distance t rad Distance t land uses Interest rate Neighbrhd Schl district Cunty Envirnmental 15

16 Variables Bensn, et al Bnd, et al Studies Referenced in Benefits Transfer Paper Kashian 2007 Lansfrd and Jnes 1995 Lmis and Feldman 2003 Muller 2007 Feather 2008 Waterfrnt lcatin Waterview Feet f shreline Feet f expsed shreline Deviatin frm average lake level Bluff lcatin Scenic view Distance t shreline Distance t central city Glf curse lcatin Water quality Shre type Bank height Rate f accretin Ersin status 16

17 References Bensn, Earl D.; J. Hansen; A. Schwartz, Jr.; and G. Smersh Pricing Residential Amenities: The Value f a View. Jurnal f Real Estate Finance and Ecnmics 16(1): Bnd, Michael T., V. Seiler, and M. Seiler Residential Real Estate Prices: A Rm with a View. Jurnal f Real Estate Research 23(1/2): Dunning, Mark and D. Jrdan. 2010a. Reprt t the Castal Zne Technical Wrking Grup n Task 3. CDM, Federal Prgrams. Chantilly, VA. Dunning, Mark and D. Jrdan. 2010b. Reprt t the Castal Zne Technical Wrking Grup n Task 2. CDM, Federal Prgrams. Chantilly, VA. Feather, Tim Prperty Value Impacts frm Castal Ersin n Chesapeake Bay, Maryland. Prepared fr Baltimre District, Crps f Engineers. CDM Federal Prgrams, Carbndale, IL. Freeman, A.M Prperty Value Mdels. In The Measurement f Envirnmental and Resurce Values. Jhns Hpkins University Press, Baltimre, MD. Kashian, Russell Lake Drawdwn: A Debate n the Value f 2 Inches f Water. Wrking Paper University f Wiscnsin Whitewater; Department f Ecnmics. Whitewater, WI. Lansfrd, Ntie and L. Jnes Recreatinal and Aesthetic Value f Water Using Hednic Price Analysis. Jurnal f Agricultural and Resurce Ecnmics 20(2): Lmis, Jhn and M. Feldman Estimating the Benefits f Maintaining Adequate Lake Levels t Hmewners Using the Hednic Prperty Methd. Water Resurces Research 39(9): Muller, Nichlas Using Hednic Prperty Mdels t Value Public Water Bdies: A Nte Regarding Specificatin Issues. Discussin Paper N Department f Ecnmics, Middlebury Cllege. Middlebury, Vermnt. Yung, Rbert Determining the Ecnmic Value f Water. Resurces fr the Future. Washingtn, D.C. 17