1 TI 2004-023/32 Tinbergen Institute Discussion Paper34 The Impact of Railway Stations on5 Residential and Commercial Property6 Value78910 Ghebreegziabiher Debrezion1,211 Eric Pels112 Piet Rietveld1,213141516171 Department of Spatial Economics, Faculty of Economics and Business Administration, Vrije1819Universiteit Amsterdam,202 Tinbergen Institute.21 Tinbergen Institute22The Tinbergen Institute is the institute for23economic research of the Erasmus Universiteit24Rotterdam, Universiteit van Amsterdam, and Vrije25Universiteit Amsterdam.262728Tinbergen Institute Amsterdam29Roetersstraat 31301018 WB Amsterdam31The Netherlands32Tel.: +31(0)20 551 350033Fax: +31(0)20 551 3555343536Tinbergen Institute Rotterdam37Burg. Oudlaan 50383062 PA Rotterdam39The Netherlands40Tel.: +31(0)10 408 890041Fax: +31(0)10 408 903142434445Please send questions and/or remarks of non-46scientific nature to driessen@tinbergen.nl.47Most TI discussion papers can be downloaded at48http://www.tinbergen.nl.49 The Impact of Railway Stations on Residential and Commercial Property Value: a Meta5051 Analysis52535455 Ghebreegziabiher Debrezion5657 Eric Pels5859 Piet Rietveld6061 Free University, Department of Spatial Economics6263 De Boelelaan 1105, 1081 HV Amsterdam6465 August 20036667 Abstract6869 Property values are affected by the properties’ physical, accessibility and environmental features. Railway stations7071 function as nodes in transport networks and places in an urban environment. They have accessibility and7273 environmental impacts, which contribute to property value. The literature on the effects of railway stations on7475 property value is mixed in its finding in respect to impact magnitude and direction, ranging from a negative to an7677 insignificant or a positive impact. This paper attempts to explain the variation in the findings by meta-analytical7879 procedures. Generally the variations are attributed to the nature of data, particular spatial characteristics, temporal8081 effects and methodology. The data can vary in relation to the type of property under study. Some indication is8283 found to support the assertion that commercial properties enjoy a higher positive impact compared to residential8485 properties. Commuter railway stations have a consistently higher positive impact on the property value compared8687 to light and heavy railway/Metro stations. The inclusion of other accessibility variables (such as highways) in the8889 models reduces the level of reported railway station impact. However this effect becomes insignificant for9091 properties within a quarter-mile of the station, leaving the railway station as a major accessibility point. Our9293 analysis did not find a significant effect of spatial and temporal factors.9495969798Key words: property value, railway station, accessibility, light railways, heavy railway/ Metro,99100commuter railway, meta-analysis.101 1. INTRODUCTION102103 Location choice is a frequently discussed topic in urban economics. Generally, these104105discussions are normative or descriptive in nature. Some of the studies address the issue of optimal106107location conditional on a given set of constraints (Fujita 1989). Others are devoted to explaining the108109character (value) of a property at a given location. However, the issue of identifying the factors that110111affect property values is common to both sets of approaches. This paper discusses studies of the latter112113category, focussing on properties surrounding railway stations.114115 The term “property” should be defined at the outset of our discussion. Different definitions116117are encountered depending on the related discipline. Property in this context means an estate ranging118119from a vacant piece of land to an area occupied by all sorts of buildings: residential, commercial,120121industrial etc. (Brigham 1965). Many property value studies have been conducted. Most authors122123agree in listing the broad categories of factors affecting property value, namely physical,124125environmental and accessibility factors (Fujita 1989, Bowes and Ihlanfeldt 2001). However some126127authors have included historical factors and land use patterns into their analysis (Brigham 1965).128129Numerous detailed lists can be identified within each of these categories. As to the relevance of the130131factors to the analysis, the detailed list can differ from one place to another and thereby from study to132133study. These factors potentially trade off between each other.134135 Proximity to a railway station as a factor affecting property value has drawn some attention136137in the literature. This paper surveys studies on the effect of railway station proximity on property138139value. The question addressed in these studies was: what is the impact of railway station proximity140141on property value? In subsequent sections, we systematically discuss the theoretical foundation of the142143studies, presenting and comparing the empirical results of the various studies conducted. We also144145discuss future study prospects based on the result of the comparisons. In addition to reviewing the146147studies conducted in the area, we make a quantitative analysis of the results of the studies, using148149150151152 2153 meta-analysis to explain the differences in the results. Thus the paper has two parts, a qualitative154155review and a quantitative analysis.1561571581592. THEORETICAL BACKGROUND OF THE STUDY160161 The theory based on farmland study pioneered by Von Thünen (1863) is the well-recognized162163first attempt in this area. The theory stresses the primacy of transportation cost in determining land164165value and use. ‘According to Von Thünen, for a given land of a given fertility, land value166167differentials are derived from the transportation savings afforded by the location of the land’ (Grass1681691992). In subsequent studies, economists improved the model in bid-rent terms (Alonso 1964 and170171Muth 1969). The basic idea behind the bid-rent model is that every agent is prepared to pay a certain172173amount of money depending on the location of the land. This leads in equilibrium to a rent gradient174175that declines with distance from the central business district for sites that yield equal utility. Up to176177this point the dominant factor explaining the difference between land (property) values was the178179accessibility as measured by the distance to the Central Business District (CBD) and the associated180181transportation costs. The physical characteristics of the land (fertility in the case of Thünen) were182183assumed given.184185 However, as the hedonic pricing approach become popular, the physical characteristics of the186187property were integrated as important components in explaining the difference in property values.188189Along with the accessibility factor, the physical characteristics are also explained in relation to the190191distance from the CBD. The bid-rent model assumes size of parcels (properties) increase as the192193distance to the CBD increases (Fujita 1989). For urban properties, the transport cost perspective (as a194195measure of accessibility) seems narrow, however. In successive studies, a more general concept of196197accessibility was introduced. The concept of accessibility thus encompasses all variables that198199contribute to the potential of opportunities of a location for interaction (Hansen 1959 and Martellato200201et al., 1998). Though a comprehensive definition of the concept of accessibility is available, the lack202203204 3205 of data and appropriate measuring technique implies that simple measures are used. Thus, in the206207literature we see a focus on some factors only, especially a CBD oriented interaction related to208209employment and shopping. In most property value studies, the social interaction variables were210211missing from the model.212213 The basic theory in real estate price studies can be put forward as follows. As a location214215becomes more attractive, as a result of certain characteristics, demand increases. This results in price216217increase. In most cases CBDs are the centres of many activities. Thus closeness to the CBD is218219considered as an attractive quality that increases property prices. Investments in transport220221infrastructure reduce this friction at the CBD to some degree (Fejarang 1994). Properties close to the222223investment area also enjoy benefits from these investments. Being close to a transport facility224225increases the accessibility of the property and thus the value of the transport facility is capitalized in226227the property value. It may be expected that a price curve will have a negative slope; when we move228229away from the station, prices decrease. However, because areas close to a station are attractive and,230231naturally, transport stations lead to polycentric structures, a linear polycentric city is likely to have a232233price curve (rent gradient) with a sinusoidal pattern. Local peaks occur at the station areas, and the234235global peak will be found around the CBD.236237238239240 Property Value241242243244245 S S CBD S S Location246247Figure 1: sinusoidal patter of property value along a line station centred by the CBD248249250 The accessibility measurement problem also appears to be a major challenge. In bid-rent251252models accessibility variables had to be measured as the distance (or the cost of transportation) to the253254255256257 4258 destination. This approach is applied for most accessibility studies. It is important to note, however,259260that this approach stems from the definition of the concept of accessibility: the ease by which the261262CBD may be reached. However, in the literature we observe different definitions of accessibility,263264depending on the purpose of the study (Rietveld and Bruinsma 1998). In some cases we may need265266additional parameters (other than cost, travel distance/time, fare etc.), including interaction patterns.267268However, even though the concept seems subjected to definition and measurement problems, its269270basic theoretical relationship to the property value remains the same. A higher accessibility index for271272the property in question implies a higher property value.273274 In the earlier property value studies, environmental amenities were not included. However275276the hedonic price approach calls for their inclusion. The concept of environmental amenities is277278subjected to more definition and measurement problems than the concept accessibility. Having279280succeeded in defining and measuring the concept of environmental amenities, the relation to the281282property value is explained in the same manner: the higher the favourable amenity index283284corresponding to the property, the higher its value. The dominant theoretical framework for most285286studies remains the one that includes physical features, accessibility and amenity factors.2872882892903. RAILWAY STATION AND PROPERTY VALUE291292 Recently, in different places, urban planning is gaining popularity as a tool to increase293294efficient land use. This is due to the increasing fragmentation of urban developments. The larger part295296of the literature on railway systems focuses on it as a feasible solution to the rising congestion posed297298by automobile traffic and urban sprawls. Railway transport investment is expected to support a more299300compact urban structure and therefore it serves the urban planning purpose (Goldberg 1981). The301302theory on land prices and settlement indicates that a higher accessibility of a location leads to a dense303304settlement. One way of investigating this assertion is to measure the premium that residents are305306willing to pay to remain close to the railway stations. Naturally, railway stations have an effect on307308309 5310 both land value and land use patterns (Ferguson et al, 1988). Our study, however, focuses on land311312value effects. Thus, we defer the land use effect for later studies. Grass (1992) indicates that public313314infrastructure has a profound influence on the pattern of urban development and spatial distribution315316of urban property values. ‘The presence of other facilities that increase accessibility like highways,317318sewer services and other facilities influence the impact area in the same fashion.’ The benefits of319320these facilities and services are also capitalized into urban property values (Damm et al, 1980). Thus,321322in order to conclude anything about the effect of a railway station on property values, one would323324need to separate the accessibility benefit attributed to the railway station. Whatever the result of the325326study will be in relation to the significance of the railway station effect, at this point we would like to327328emphasize the relationship between the railway (as a public investment) as a transport means and the329330real estate market.331332 In recent literature, a station is seen from two angles: as a node in a transport system and a333334place in an area (Bertolini and Spit 1998). In line with this approach, stations pose two types of335336effects on nearby properties, namely accessibility and amenity. This relatively new approach solves337338the difference in relative impact of stations to property values in different places and over different339340periods of time. As pointed out earlier, the first impact was dominant in the earlier property value341342studies. However, recent studies also emphasize the latter impact, as we now will explain.343344 Property values are affected by the inherent characteristics of the property and by positive or345346negative external amenities. The inherent characteristics of the property are the physical347348characteristics they offer. Many hedonic price analyses for property values (specially residential349350areas) have listed a broad range of these characteristics (Grass 1992, Kruk 2001). The external351352amenities that affect the value of property emanate from the surrounding physical environment and353354the activities undertaken there. The intensity of the external amenity emanating from an area (railway355356station for example) on property tends to vary. Immediate locations are expected to have higher357358359360361 6362 effects than locations further away. The high population movement gives rise to the development of363364retail activities but may at the same time attract criminality (Bowes and Ihlanfeldt 2001).365366 Accessibility to the place of interest (predominantly the CBD) is determined by different367368travel alternatives (railways, highways, etc). Some property value studies determine the impact of the369370variables on property values by measuring the impact of each separate variable on property value. On371372the other hand, some studies keep some accessibility variables constant, and only capture the impact373374of the remaining variables (in most cases railway stations). As Voith (1993) has pointed out, highway375376accessibility is an important competitor to rail accessibility. However, in some recent studies no377378explicit consideration of the highway accessibility impact share in property value was given379380(McDonald and Osuji, 1995; Bowes and Ihlanfeldt, 2001).381382 Different properties can surround railway station areas. Due to the unique nature of the383384property, interaction with the railway station can have substantially different impacts on the property385386value. Though Brigham mentions, in passing, that property value models could behave differently for387388different property types, his empirical study is devoted to residential property values (Brigham,3893901965).3913923933944. EMPIRICAL FINDINGS ON PROPERTY VALUES395396 The empirical studies conducted in this area are diverse in methodology and focus. In order397398to single out the effect of proximity to a railway station, the impact of other characteristics of the399400property under consideration are often incorporated in the models. Although the functional forms can401402differ from study to study, the most common methodology encountered in the literature is hedonic403404pricing. The empirical studies on the impact of railway stations range in scope from broad economic405406matters of employment and settlement densities (Bollinger and Ihlanfeldt 1996) to specific impact on407408property values (Bowes and Ihlanfeldt 2001).409410411412413 7414 The studies in property values mainly consider three groups of determinants: the physical415416characteristics, accessibility and environmental amenities. In the physical characteristics category,417418the inherent factors representing the qualitative and quantitative features of property are identified.419420Environmental amenities are the externalities (bad or good) that emanate from the neighbourhood.421422Railway stations possess two impact characteristics: an environmental amenity and an accessibility423424character. Most of earlier studies consider the accessibility variable only. The impact of railway425426stations on property values becomes smaller as one goes farther away from the station. However, the427428effect of railway station does not necessarily have a circular pattern, especially if the lines run above429430ground. The negative environmental externalities, on the other hand, can have an effect on properties431432along the railway line.433434 Generally speaking, no consistent relationship between proximity to railway stations and435436property values is recorded in the literature. Furthermore, the magnitudes of these effects can be437438minor or major. No clear consensus exists, therefore. Studies on residential property value usually439440show that property value increases in response to transportation cost savings (Boyce et al., 1972).441442One of the earliest studies, Dewees (1976), also analysed the relationship between travel costs by rail443444and residential property values. Dewees found that a subway station increases the site rent445446perpendicular to the facility within a one-third mile walk to the station. Similar findings confirmed447448that the distance of a plot of land from the nearest station has a statistically significant effect on the449450property value of the land (Damm et al, 1980). Consistent with these conclusions, Grass (1992) later451452found a direct relationship between the distance of the newly opened metro and residential property453454values. Some of the extensively studied metro stations in the U.S., though ranging from small to455456modest impact, show that properties close to the station have a higher value than properties farther457458away (Giuliano 1986; Bajic 1983; Voith 1991a). However there are studies, which have also found459460insignificant effects (Lee 1973; Gatzlaff and Smith 1993). On the other hand, contrary to the general461462assumption, Dornbusch (1975), Burkhart (1976) and Landis et al, (1995) traced a negative effect to463464465 8466 the proximity of stations. Evidence from other studies indicates little impact in the absence of467468favourable factors (Gordon and Richardson 1989; Guiliano 1986). For detailed documentation of the469470findings, we refer to (Vessali 1996; Smith 2001; NEORail 2001; CIP annual conference 2002; RICS4714722002). In general, some studies indicate a decline in the historical impact of railway stations, on473474property values. This was attributed to improvements in accessibility, advances in475476telecommunications, computer networks, and other areas of technology that were said to make477478companies “footloose” in their location choices (Gatzlaff and Smith 1993).479480 The larger part of the empirical literature on property value focuses on residential properties481482rather than commercial properties. Generally, it is claimed that the range of the impact area of483484railway stations is larger for residential properties, whereas the impact of a railway station on485486commercial properties is limited to immediately adjacent areas. In addition, there are claims that487488railway stations have a higher effect on commercial than on residential properties (Weinstein and489490Clower 1999; Cervero and Duncan 2001). This finding is in line with the assertion that railway491492stations - as focal, gathering points - attract commercial activities, which increase commercial493494property values. However contrary to this assertion, Landis et al, (1995) determined a negative effect495496on commercial property values.497498 In subsequent studies addressing the variation in the findings, the noise and other negative499500externalities of railway stations were treated separately from the accessibility and other positive501502amenities. Bowes and Ihlanfeldt (2001) pointed out the retail employment and crime that stations503504attract. A significant relation was observed between stations and crime rates. However, no proximity505506variable shows a significant effect on retail employment. In this model, the immediate507508neighbourhood is affected by the negative impact of the station. Thus the most immediate properties509510(within a quarter of a mile of the station) were found to have an 18.7% lower value. Properties that511512are situated between one and three miles from the station, however, are more valuable than those513514515516517 9518 further away. Though this study provides an important contribution, unexplained variations still519520remain.521522 Heterogeneity between stations is another important characteristic that specifies differences523524between study results. Stations differ from each other in railway technology, service frequency and525526service catchment areas. We can identify four types of railways in the studies, namely Light railways527528(LRT1), heavy (rapid) railways (HRT/Metro2), commuter railways (CRT3) and rapid bus transits529530(BRT4). There are indications that commuter railways have a higher impact on property value than531532other stations (Cervero 1984; Cervero and Duncan 2001; NEORail II 2001). The number of parking533534lots in or near the station and the proximity of the railway station to the CBD also increase the impact535536of the station on property value (Bowes and Ihlanfeldt 2001). In addition Gatzlaff and Smith (1993)537538claim that the variation in the findings of the empirical work is attributed to local factors in each city.539540 Some empirical studies on a railway station’s impact on property value also focused on541542demographic characteristic differences in the population of various city quarters. Income and social543544(racial) divisions are common in the literature. Proximity to a railway station is of higher value to545546low-income residential neighbourhoods than to high-income residential neighbourhoods (Nelson5475481998; Bowes and Ihlanfeldt 2001). The reason is that low-income residents tend to rely on public549550transit and thus attach higher value to living close to the station.5515525535545. META-ANALYSIS OF THE STUDIES555556 In the previous section we briefly reviewed empirical work on the effects of station proximity557558on property value. Other reviews can be found in Vessali (1996), Smith 2001, NEORail (2001), CIP559560annual conference (2002) and RICS (2002). These studies also summarized empirical work in this561562area, but did not look for a systematic explanation of the variation in the findings. Our study not only563564summarizes earlier work, but also looks for a systematic explanation of differences in the results.565566Meta-analysis serves as an important tool for this purpose (Smith and Huang 1995; Cook et al, 1992).567568569 10570 It provides statistical synthesis of empirical research focused on a common research question. It571572includes the differences in study settings that are expected to explain the variation in the findings of573574the studies. In our case, the summarized studies focus on the impact of railway station proximity575576relative to property value. Different approaches towards the analysing of property value in areas577578surrounding railway stations are encountered in the literature. For the comparison of results to be579580meaningful, it is required that the studies have comparable effect sizes (in our case the change in581582property value at various distances from the railway station). Different measurement units are583584encountered in the literature, although these all aim to measure a similar effect. However, for meta-585586analysis it is important that the findings are in the same measurement unit. Meta-analysis models are587588basically hedonic in nature and the results of the studies are treated as a dependent variable explained589590by implicit or explicit characteristics of the underlying studies. A basic meta-analysis equation can be591592given as follows (Florax et al, 2002).593594595596Y = f ( P, X , R, T , L ) + ε (1)597598Where Y= the variable under study599600 P= set of causes of the out come Y601602 X= characteristics of the set of objects under examination affected by P in order to603604 determine the outcome Y605606 R= characteristics of research method607608 T= time period covered by the study609610 L= the location of each study conducted611612 ε = the error term613614615616617 11618 The number and nature of the studies on railway station impact on property value enable the619620use of meta-analysis. The studies included in this study all quantify the impact of railway station621622proximity on property values. A matching process was necessary to transform the study results into a623624variable with a common unit of measurement.6256266276285.1 Model specification629630 The literature on property value estimation is dominated by a hedonic price approach. This631632approach treats a certain property as a composite of characteristics to which value is attached. The633634value of the characteristics explains the value of the property as a whole. Meta-analysis is also635636hedonic in nature, since it starts by identifying the characteristics of the underlying studies that could637638explain the variations in effect size. The underlying studies usually include the proximity of the639640property to the station. However, we observe that not all studies use the same set of (explanatory)641642variables. The studies also differ in methodology. A railway station variable is mostly treated as an643644indicator of the degree of accessibility of a certain area. Likewise, other variables serve the same645646purpose (e.g. presence of highways/freeways in the area under consideration). One can thus expect647648that these variables “interact”. Although they are complementary (one can take a car to the railway649650station and then take the train), for our purpose it is important to note that they both have an effect on651652property values. These effects could reinforce each other, but may also be “competitors”.653654 The underlying empirical studies employ different specifications, namely linear, semi655656logarithmic and log linear. In some studies the analyses are non parametric in nature. Different657658specifications may also lead to different outcomes. In our analysis we further include type of railway659660station (light rail, heavy rail/Metro, commuter rail), type of property (commercial, residential),661662geographical location (Europe versus North America) and time of study. We also examine whether663664the underlying study includes variables for the quality of a property and demographic features. Thus,665666our analysis includes eight categories of variables to explain the difference in the findings of the667668669 12670 impact of railway station proximity on property values. To account for the variation we specify a671672standard hedonic model using a simple linear form where bold symbols represent vectors of673674variables:675676677678Y = α 0 + β1 P + β 2 S + β 3 M + β 4 ACCESS + β 5 DM + β 6 HQ + β 7 L + β 8T + ε (2)679680Dependent variable681682Y is the percentage impact of railway station on property value (rent).683684Explanatory variables5685686P is a dummy variable that takes on the value 1 when commercial properties are analysed (reference687688group is residential properties). S is a vector of dummy variables for the station type (heavy rail/689690Metro, commuter rail, BRT; light rail is the reference group). M is a vector of dummy variables for691692the model type (semi log, double log, non parametric; linear is the reference group). ACCESS is a693694dummy variable indicating the presence of other means of access to the area in the underlying study695696(usually highways and/or freeways). DM is a dummy variable indicating the presence of a697698demographic variable in the underlying study (usually income or racial composition of city quarters).699700HQ is a dummy variable for the presence variables indicating the quality of a property in the701702underlying model. L is a vector of dummy variables indicating the geographical location of the study703704(Western US, Central US, Europe; Eastern US is the reference group). T is a dummy for time trend705706(assume 1 for study data after 1990, study data before 1990 is taken as a reference group).707708 Some of these variables were used in the models of the underlying studies. However others709710were not used in these studies, although the study contains enough information to allow these711712variables to be used in the meta-analysis. Because most variables in the meta-analysis are dummy713714variables, the estimated coefficients represent the percentage contribution of each attribute on715716property value in comparison to the reference groups.717718719720 13721 5.2 Data and methodology722723 The database for the analysis of this paper is a pool of studies on the impact of railway station724725proximity on property value. A wide range of studies is covered. A total of 102 estimation results726727were obtained from the underlying studies. Different specifications in the same underlying study are728729treated as separate observations. Thus, the total number of underlying studies is lower than the730731number of observations in our meta-analysis. However, due to the incompleteness of some of the732733studies with respect to the requirements of this study, we had to exclude certain observations. We734735finally made use of 73 observations.7367375.2.1 Variation in the presentation of the findings738739 The dependent variable in our meta-analysis is expressed as the percentage change in740741property value per some distance measure to the station. The underlying studies are quite diverse in742743the way the impact of railway station proximity is reported, including pure monetary effects,744745percentage effects and elasticity measures. However, the larger part of these studies reports the746747percentage increase or decrease in property value for a certain distance. In addition to the diversity of748749measurements, the studies also use a variety of methodologies. We summarize them in two750751categories:752753754755I. Parametric estimation methods756757 These studies use econometric methods to estimate the impact of railway station proximity758759on property value. Linear, semi log and log linear (also called double log) specifications are760761common. Three broad categories of railway station proximity measurement were encountered.7627637647651. Station effect as a continuous measure:766767 These studies consider the proximity to a railway station as a continuous variable. The768769variable can be measured in distance, time (walking time) or monetary savings (Dewees 1976;770771772 14773 Nelson and 1990; Nelson 1992; Benjamin and Sirmans 1996; Lewis-Workman and Brod 1997; Chen774775et al, 1998; Gatzlaff and Smith 1993). The results are given in monetary units (as in linear models) or776777in percentage units (as in semi log and log linear models). The results of the semi log models are in778779line with the dependent variable in our meta-analysis. Therefore the monetary changes and elasticity780781have to be transformed into a percentage change per distance using the average property value and782783average distance data reported in each underlying study. Coefficients of semi log and double log784785specifications represent incomparable measures. Thus to bring them into comparable units we786787divided the elasticity by the average distance of the impact area. The rent curves can have structures788789similar to (a) in figure 2 below.790791Table 1. Sample of railway station effect on property value based on continuous proximity792measures793Author Railway station impact on property value794Dewees (1976) $2370 premium per hour of travel time saved for sites within 20 minutes795 travel time (e.g. 1/3 mile walk)796Nelson (1992) $1.05 per feet distance to the station. premium on property value in low-797 income areas;798 $.96 per feet distance to the station.799Allen, et al, (1986) $443 premium on property value for every dollar saved in daily commute800 costs (average >$4,500 per house; 7.3% of mean sales price)801Lewis, Workman and Brod (1997) Elasticity of 0.22 w.r.t property value and distance802Benjamin and Sirmans (1996) Rent decreased by 2.4 to 2.6% for each one tenth mile distance from the803 metro station8048058068078082. Station effect as a category measures:809810 These studies treat the proximity variable as a discrete variable (represented by dummy). The811812area under consideration is segmented into two or more parts, where the outer segment is treated as813814the reference (McDonald and Osuji 1995; Fejarang et al, 1994; Dueker and Bianco 1999; Weinstein815816and Clower 1999; Voith 1993; Armstrong 1994; Grass 1992; Bowes and Ihlanfeldt 2001; Cervero817818and Duncan 2001, 2002; Weinberger 2001). The rent curve for these types can be given by (b) in819820figure 2 below.821822823824825 15826 Table 2. Sample of railway station effect on property value based on category measures827Author Result828Cervero, Robert (1996) +10- 15% in rent for rental units within 1/ 4 mile of BART829830Bowes and Ihlanfeldt (2001) Property value effect (percentage change)831 0-1/4 mile -18.7%832 ¼-1/2 mile 2.4 %833 ½-1 mile 0.9 %834 1-2 mile 3.5%835 2-3 mile 3.5%836Weinberger (2001) Rent837 0-1/4 mile +13 cents per square foot838 ¼-1/2 mile +7 cent per square foot839 ½-3/4 mile + 1 cent per square foot840 ¾-1 mile No effect841842843844 Rent Rent845846847848849 Distance Distance850851852Figure 2. Structure of rent curves. Distance from the station as a continuous measure (left panel)853and as category measures (right panel).854855856II. Non-parametric measures:857858 These studies do not use econometric methods to estimate the effect of railway stations on859860property values. They can measure the proximity variable in continuous or discrete terms. The861862common feature of these studies is that the difference in property value is implicitly attributed to the863864railway station effect only. Some examples of this sort are given in Table 3.865866Table 3. Sample of results presentation for non-parametric cases867Author(s) Result868Weinstein and Clower (1999) Effect of station on property vale869 Within ¼ mile of the station (percentage change)870 Retail 36.75%871 Office 13.85%872 Residential 5.97%873 Industrial 7.68%874875Dueker and Bianco (1999) Property value declines $1593 for every 200 feet out of the station876877Fejarang (1994) Properties within ¼ mile of the station enjoy premium of $31 per square foot.878879880881882 16883 5.2.2 Meta-analysis dependent variable884885 For meta-analysis it is essential that the dependent variable is measured in comparable units. Due886887to the large differences between in the underlying studies in reporting the finding some conversion888889mechanism is required. Three reasonable considerations were necessary for this mechanism:890891 1. We consider railway station impacts up to a maximum distance of two miles, unless892893 otherwise indicated.894895 2. The properties under study are evenly distributed in concentric circles around the railway896897 stations. Thus, due to the fact that larger circles lead to an area enlargement, the average898899 distance to the station for each segment is given by a+2/3*(b-a), where a is the distance the900901 border of the inner concentric circle to the station, and b is the distance between the border of902903 the outer segment and the railway station. For the station itself we have a=b=0.904905 3. The impact of a station in the same segment in a circle is uniform.906907 For studies that provide the impact for several segments, the continuous railway station impact908909(see for example Table 2) is estimated by the approach outlined in Appendix 1. However for studies910911that looked at one (immediate) segment, as compared to the outer segment, we have estimated the912913continuous station effect per distance by point estimation (under the above assumptions). A final914915choice is based on the unit of distance. We decided to adopt a unit of measurement equal to 250916917metres. Thus, the dependent variable in the meta-analysis is the percentage change in property value918919(rent) per 250 metres distance to the railway station. In addition we have prepared the effect of the920921railway station on the immediate segment (within a quarter mile of the station). Therefore, our922923estimation is based on these two data sets.924925926927928 17929 5.2.3 Independent Variables930931In this context, the impact of railway proximity on station on property value as reported in the932933underlying studies can be affected by several factors. The type of property value under study may be934935important, because commercial and residential properties may be affected differently. Different types936937of railway stations may have different impacts because the frequency of service or the passenger type938939may be different, etc. The various types of railways are defined in Endnotes 1-4. The type of model940941used to determine the effect can actually influence the effect (compare e.g. point elasticity estimates942943to interval estimates). Although most studies were parametric, a few studies used a non-parametric944945model, as discussed above. Three types of parametric models were encountered: linear, semi-log and946947log linear. Temporal and spatial characteristics have to be included, because the effects in Los948949Angeles and London could, for instance, be very different due to inherent characteristics of these950951cities. We also included a variable for the presence of other accessibility variables (highways and952953freeways are of interest here), house quality, and demographic features in the underlying studies, as954955discussed above. As shown in Table 4, these considerations lead to eight categories of dependent956957variables in our meta- analysis.958959960961962 18963 Table 4 Independent variables964 Variable Description Type965Type of property (P)966 RESIDENTIAL Residential property Dummy967 COMMERCIAL Commercial Property Dummy968Type of station (S)969 LRT Light rail transit station Dummy970 HRT Heavy (rapid) rail transit Dummy971 station/ Metro972 COMMUTER Commuter rail transit station Dummy973 BRT Rapid Bus transit station Dummy974Type of underlying model (M)975 LINEAR Model with linear specification Dummy976 SEMI LOG Model with semi log specification Dummy977 LOG LINEAR Model with log linear specification Dummy978 NON Non parametric Dummy979 PARAMETRIC980Location of study (L)981 US1 Eastern United States Dummy982 US2 Central United States Dummy983 US3 Western United States Dummy984 EUROPE Europe Dummy985Inclusion of Accessibility Variable(s) in the underlying model986 ACCESSIBILITY (ACCESS) Dummy987Inclusion of Demographic variable(s) in the underlying model: income, racial composition988of city quarters989 DEMOGRAPHIC (DM) Dummy990Inclusion of House quality variable(s) in the underlying model991 HOUSE quality (HQ) Dummy992Time of data (T)993 TIME Before 1990 Dummy994 TIME After 1990 Dummy9959969975.3 Descriptive statistics998999In Table 5 we display the distribution of the dependent variable. Overall characteristics and10001001characteristics per group (defined by the independent variables) of the dependent variable are given.10021003The mean impact of a station on property value (rent) for every 250 metres close to the station is100410052.61%. The Table shows that the range is considerable; it varies from –12.84% to +38.70%.10061007100810091010 191011 Table 5: Descriptive summary of Railway station proximity impact on property value1012 Effect per 250 metre Effect within 1/4 mile1013 a1014 Min Mean t test Max Stdev N Min Mean t test Max Stdev N1015Over all -0.128 0.026 0.387 0.059 73 -0.619 0.089 1.452 0.235 7010161017Property Type1018Residentialb -0.038 0.019 0.099 0.030 52 -0.193 0.062 0.429 0.109 491019Commercial -0.128 0.043c 1.542 0.387 0.098 21 -0.619 0.152 1.489 1.452 0.395 211020Residential1021Properties1022Single Familyb -0.031 0.018 0.099 0.030 27 -0.149 0.060 0.370 0.093 271023Multi Family 0.000 0.002 1.284 0.013 0.005 6 0.000 0.011 1.252 0.065 0.027 61024Condominium -0.038 0.008 0.661 0.084 0.041 6 -0.193 0.043 0.300 0.429 0.209 61025Unidentified -0.012 0.035 0.083 0.024 13 -0.039 0.110 0.291 0.095 101026Type of railway1027stations1028LRTb -0.014 0.025 0.111 0.036 26 -0.072 0.076 0.368 0.106 231029HRT -0.128 0.015 1.039 0.099 0.040 28 -0.619 0.053 0.547 0.370 0.172 281030CRT -0.056 0.054 1.292 0.387 0.104 15 -0.270 0.194 1.288 1.452 0.421 151031BRT -0.031 0.005 1.070 0.041 0.030 4 -0.149 0.025 0.842 0.200 0.144 41032Model1033Non Parametric 0.016 0.061 1.565 0.111 0.042 6 0.060 0.155 0.673 0.368 0.124 51034Linearb -0.128 0.019 0.387 0.064 53 -0.619 0.073 1.452 0.268 511035Semi Log -0.006 0.039 0.892 0.099 0.038 9 0.000 0.136 0.693 0.370 0.111 91036Log Linear 0.021 0.038 0.666 0.051 0.013 5 0.050 0.103 0.246 0.165 0.048 510371038Accessibility b -0.128 0.020 0.387 0.065 51 -0.619 0.077 1.452 0.266 511039No Accessibility -0.012 0.041 1.432 0.111 0.036 22 -0.039 0.122 0.720 0.370 0.115 1910401041Demographic b -0.128 0.019 0.387 0.070 44 -0.619 0.076 1.452 0.286 441042No Demographic -0.012 0.038 1.366 0.111 0.032 29 -0.039 0.111 0.602 0.370 0.104 2610431044House quality b -0.128 0.022 0.387 0.061 62 -0.619 0.085 1.452 0.249 611045No House quality 0.016 0.052 1.428 0.111 0.036 9 0.022 0.137 0.546 0.368 0.113 710461047Study Place1048US1b -0.006 0.031 0.099 0.024 19 0.000 0.102 0.370 0.101 191049US2 0.016 0.045 1.076 0.098 0.033 5 0.060 0.161 1.105 0.368 0.123 51050US3 -0.128 0.022 0.521 0.387 0.070 47 -0.619 0.077 0.378 1.452 0.283 451051Europe 0.024 0.025 0.358 0.025 0.001 2 0.022 0.022 0.022 . 11052Time1053Up to 1990b 0.000 0.027 0.111 0.031 29 0.000 0.066 0.370 0.080 271054105510561057 a1058 The t statistics for pair-wise mean equality test of one group with the reference type within the group. For Example the null hypothesis for1059 property type is H0: mean change of value for commercial properties = mean change of value for residential properties. The critical values at 5%1060 significance level for d.f. 19, 22, 28, 31, 39, 52, 64, 69, and 71, are 2.093, 2.074, 2.048, 2.040, 2.023, 2.007, 1.998, 1.995 and 1.994 respectively.1061 b1062 Variable reference in the group (category)1063 c1064 Bold figures in the column represent higher mean in each group (category)1065106610671068 201069 From the table above we learn that railway stations have a higher average effect on commercial10701071properties compared to residential properties. However, the corresponding standard deviations are10721073quite high. Commuter railway stations have higher impact on property values than the other three10741075types of railway stations. Non-parametric methods report higher station effects. This is because these10761077analyses attribute the differences in property value solely to station proximity, so that result may be10781079overstated. Contrary to the literature assertion that railway stations have lower impact on multi10801081family or condominium properties as compared to single-family properties, the Table indicates10821083higher impact on single-family properties (Cervero 1997; Cervero and Duncan 2002). Studies in the10841085central part of the US report higher effect of railway stations.10861087108810895.4 Estimation Results10901091 To test for the significance of the differences in the findings of the studies, we estimate two10921093functions. The first function explains the impact of station proximity on property value (rent) for10941095every 250 metres distance reduction to the station, using the variables selected above as explanatory10961097variables. The second function explains the impact of station proximity on property value (rent) for10981099properties within ¼ mile of the station, again from the variables above. Weighting is also considered11001101based on the square root of the number of observations in the underlying studies. The output of the11021103model after stepwise elimination of insignificant variables is given in the following tables.11041105 Table 6: Unweighted estimation output for Table 7: Weighted estimation output for1106 impact of station on property value for every impact of station on property value for every1107 250 m 250 m1108 Variable Parameter Standard t Variable parameter standard t1109 estimate error test estimate error test1110 Constant 0.03312 0.0122 2.723 Constant 0.03175 0.0136 2.3181111 Commercial 0.02856 0.0141 2.027 Commercial 0.02443 0.0135 1.8071112 Commuter 0.05268 0.0167 3.147 Commuter 0.04074 0.0136 2.9941113 Accessibility -0.03732 0.0147 -2.544 Accessibility -0.03836 0.0147 -2.6061114 R2=0.18, 73 observations R2=0.19, 71 observations11151116111711181119 211120 In Tables 6 and 7 we see that the weighted and unweighted estimates of the function for the distance11211122reduction measured in steps of 250 metres are very similar. All coefficients seem to be statistically11231124equal. Each coefficient (apart from the constant) gives the percentage increase compared to the11251126reference group.11271128 Commuter railway stations have a higher impact on property values than light or heavy11291130railway/ Metro stations (light railway is the reference group, heavy railway/ metro is insignificant).11311132This finding is consistent with the expectation, and reflects the fact that commuter railways usually11331134have wider service coverage (i.e. a larger catchment area).11351136 The inclusion of other accessibility factors (highway, freeway) in the underlying studies11371138significantly reduces the level of the reported station impact on property values (reference is “no11391140alternative accessibility variable in underlying study”). This shows that highways and freeways are11411142also important determinants of property value (rent), next to railway station proximity. When both11431144are included in the models (railway station and other modes), the effect on property value is “shared”11451146between the two different modes. Models with highway accessibility on average report 4% lower11471148railway station proximity effects on property value than models excluding highway accessibility.11491150This result becomes insignificant when an alternative the specification is used with discrete railway11511152station distances within a quarter mile of the station areas, as can be seen in Tables 8 and 9. This11531154shows that competition between railway and highway declines in the inner circle areas.11551156 Table 8: Unweighted estimation output for Table 9: Weighted estimation output for1157 impact on property value for properties impact on property value for properties1158 within ¼ mile of the station within ¼ mile of the station1159 Variable Parameter Standard t Variable Parameter Standard t1160 estimate error test estimate error test1161 Constant 0.0705 0.0613 1.150 Constant 0.0530 0.0663 0.7991162 Commercial 0.1064 0.0592 1.798 Commercial 0.0754 0.0569 1.3271163 Commuter 0.1845 0.0701 2.633 Commuter 0.1639 0.0569 2.8781164 Semi log 0.0578 0.0861 0.671 Semi log 0.1221 0.0729 1.6751165 Accessibility -0.0827 0.0670 -1.235 Accessibility -0.0783 0.0674 -1.1621166 2 21167 R =0.13, 73 observations R =0.13, 73 observations1168 In both specifications, it appears that railway station proximity generally has a higher positive11691170impact on commercial property values. It should be noted, however, that the standard errors in Tables117111728 and 9 are fairly large. Although from Table 5 it appears that non-parametric studies report larger11731174effects of railway station proximity on property values, we do not find such an effect in the11751176estimations. The estimations suggest that semi log specifications may result, on average, in a 12%11771178increase. Our analysis does not find any temporal or spatial variation effect.11791180118111826 CONCLUSION11831184 The impact of railway station proximity on property value has received wide attention in the11851186economic literature. Several empirical studies tried to quantify this effect. However, the conclusions11871188are not uniform. The aim of this paper is to find a systematic explanation for the variation in railway11891190station impact findings. We established that the different features of the study settings could explain11911192these variations. We have tried to relate the variation with eight categories of variables. These are11931194type of property under consideration, type of railway station, type of model used to derive the11951196valuation, the presence of specific variables related to accessibility, demographic features and house11971198quality in the models, place of study and lastly the time of the data. The presence of accessibility and11991200house quality variables is expected to have a negative effect on the magnitude of the impact of the12011202station on the property value reported. However, out of the 14 explanatory variables included, only12031204three variables appeared to be statistically significant in the estimation procedures.12051206 Throughout the analysis, commuter railway stations have had a significantly higher impact12071208on property values compared to light or heavy railway/Metro stations. Their higher service coverage12091210adds to the attraction of the area surrounding the stations. In addition the number of commuter12111212railway stations is (naturally) low compared to light and heavy railway/Metro stations. Thus, the12131214probability that properties are closer to these stations is relatively low compared to the probability1215 that properties are located near light or heavy railway/ Metro stations. This will affect the value of12161217properties around commuter railway stations.12181219 There is some indication that commercial properties enjoy a higher positive impact due to12201221railway station proximity than residential properties do. A given area can be made accessible by a12221223number of modes (railways, car, etc.) Each mode will improve the accessibility of the region12241225independently. All of the studies used in the meta-analysis analyse the (isolated) effect of a railway12261227station on property value. When other accessibility modes are included in the underlying studies,12281229railway stations generally have a lower impact on property value. Although both highways12301231(freeways) and stations may increase property values, there is a negative correlation between the two12321233effects; when one is present, the effect of the other is diminished. Thus, we find an example of12341235omitted variable bias: when highway accessibility is not explicitly addressed, railway impacts on12361237property values tend to be overestimated specially in the continuous space specification.12381239 The research agenda that follows from these findings is as follows. To be able to do a more12401241thorough meta-analysis, a more extensive set of explanatory variables is necessary. Moreover, the12421243number of observations should preferably be increased. As far as the availability of data allows, the12441245share of public transport in the study areas can be included as a weighting factor. 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Presented at Transportation Research Board 79th Annual15061507 Meeting, Washington, D.C. January 9-13.1508150915101511Appendix 1: Deducing the continuous railway station effect from discrete measures1512151315141515The basic methodology for this was to linearize the impact over the different segments. For this15161517method to work, it is required that the studies used at least three segments, including the reference15181519segment in their analysis. Based on the assumptions described in Section 5.2.2, we can fairly say that15201521the impact of railway station proximity on properties at the average distance of the segment from the15221523station represents the effect of the station on the segment. The average distance of each segment is15241525given by d = a + 2/3*(b-a), where “a” is the distance of the inner circle to the station and “b” is the1526152715281529 291530 distance of the outer circle of the segment to the station. The reference segment’s (the segment with15311532value 100) outer circle is specified based on assumption one unless otherwise specified in the15331534underlying studies. This gives us two corresponding variables (distance and value) for which we can15351536estimate percentage change in property value per unit of the distance measure using semi log15371538specification:15391540ln(value) = a0 + b1×D15411542Where value is the value of properties at distance D from the railway station. The value of the15431544coefficient b1 measures the percentage change on property value for a unit change of distance.1545154615471548Endnotes:1549155011551 Light Rail Transit, known as LRT, is a rail-based urban transit system. It has the flexibility to navigate1552sharp curves, and travel along streets, highways or in exclusive right-of-way. Since the rails are flush with1553the surface of the street, LRT can be operated in areas with pedestrian, cyclist, or automobile activity.1554LRT is powered by electricity from overhead wiring which is suspended from poles or buildings155521556 Heavy Rail vehicles receive current from an electrified third rail. The system operates along an1557exclusive guide way and is grade separated from other vehicular or rail modes. Subway or elevated1558alignments are the common. Heavy rail is appropriate for corridors or alignments with very high demand,1559as this technology can transport a very high volume of passengers per hour at a high average speed.1560Operations can be very reliable because of complete grade separation of the alignment.156131562 Commuter Rail systems typically operate along existing freight railroad rights-of-way, serving longer-1563distance trips between central cities, suburban activity centres and outlying areas. Vehicles are1564configured to provide maximum-seated capacity and comfort due to longer trips. Commuter rail vehicles1565can operate in mixed traffic with freight trains.156641567 Bus Rapid transit BRT combines the quality of rail transit and the flexibility of buses. It can operate on1568exclusive transit ways, HOV lanes, expressways, or ordinary streets.1569(Source: http://www.utahmetro.com/Classroom/heavliterail.htm)157051571 All the dependent variables are discrete variables represented by dummies in the analysis.15721573157415751576 301577