Measuring the Effects of Light Rail Plans in Portland: A Trilogy

Gerrit KnaapProfessor and Director

National Center for Smart GrowthUniversity of Maryland

Acknowledgements

• “Does Planning Matter?” DURP, UIUC– http://www.urban.uiuc.edu/projects/portland/

portland.html

• Lew Hopkins, UIUC

• Chengri Ding, UMD

• Paul Hanley, UI

• Dick Bolen and Carol Hall, Portland Metro

Existing Light Rail System

Knaap, Gerrit J., Chengri Ding, and Lewis Hopkins

Does Planning Matter? The Effects of Light Rail Plans on Land Values in Station

Areas

Journal of Planning, Education, and Research, 21,1: 32-39.

Research Question

• Do markets respond to the information content in light rail plans?

Research Strategy

• Determine whether land values in station areas change following the announcement of station locations?

Research Design

P = 0 + iXi + j*(PROX*YEARj)+ T*TIME + u (1)

where, P = sales price per acre of land;

Xt = a vector of locational attributes;

PROX = a dummy variable which equals one if the parcel is located

within one-half or one mile of a planned station (in the

regression equations PROX is replaced by ONEMI and

HALFMI);

YEAR = a dummy variable set to one if the parcel was sold in 1992,

1993, 1994, or 1995 (in the regression equation YEAR is

replaced by 1992, 1993, 1994, or 1995);

TIME = date of sale indexed by month; 1 = January 1992;

u = a random error.

Research Design (cont.)

P = 0 + iXi + b*(PROX*BEFORE)+ a*(PROX*AFTER) + T*TIME + u (2)

BEFORE = = a dummy variable set to one if the parcel was sold before the

station locations were announced

AFTER = a dummy variable set to one if the parcel was sold after the

station locations were announced.

P = 0 + iXi + p*(PROX)+ a*(PROX*AFTER) + T*TIME + u (3)

Price Effects of Proximity by Year Dependent Variable: LOG(AMOUNT/ACRES)

Variable Coefficient t-Statistic Variable Coefficient t-StatisticC 9.0781 **** 8.28 C 8.8140 **** 7.77LOG(ACRES) -0.6446 **** -51.84 LOG(ACRES) -0.6407 **** -51.23LOG(EXP93) 0.3836 **** 5.04 LOG(EXP93) 0.3968 **** 5.07LOG(STRATIO) 0.1267 1.16 LOG(STRATIO) 0.1327 1.14FLOOD -0.1790 **** -3.94 FLOOD -0.1852 **** -3.97LOG(DENSITY) -0.0158 -0.77 LOG(DENSITY) -0.0149 -0.72ONMAJRD -0.1281 **** -4.24 ONMAJRD -0.1293 **** -4.14ONMINRD -0.1465 **** -4.96 ONMINRD -0.1412 **** -4.75LOG(MEDHOUS) 0.1923 **** 3.99 LOG(MEDHOUS) 0.1990 **** 4.04LOG(PUBDIS) -0.0388 **** -3.37 LOG(PUBDIS) -0.0404 **** -3.43LOG(LTDRATE+0.5) -0.0228 -1.74 LOG(LTDRATE+0.5) -0.0247 -1.85LOG(SALE) 0.1146 **** 10.03 LOG(SALE) 0.1149 **** 9.71LOG(PORTCBD) -0.4704 **** -10.95 LOG(PORTCBD) -0.4672 **** -10.77LOG(BEAVCBD) 0.1359 **** 4.98 LOG(BEAVCBD) 0.1390 **** 5.03LOG(SEWERDIS) 0.0040 0.61 LOG(SEWERDIS) 0.0049 0.74B92*HALFMI -0.0653 -0.53 B92*ONEMI 0.0508 0.87B93*HALFMI 0.0629 0.30 B93*ONEMI 0.0145 0.21B94*HALFMI 0.7066 **** 4.76 B94*ONEMI 0.1011 * 1.95B95*HALFMI 0.2134 **** 2.42 B95*ONEMI 0.0945 * 2.03

R-squared 0.7250 R-squared 0.7213Adjusted R-squared 0.7218 Adjusted R-squared 0.7180

Price Effects of Proximity Before and After Plan Announcement

Variable Coefficient t-Statistic Variable Coefficient t-StatisticC 9.057801 **** 8.24617 C 8.846913 **** 7.94265LOG(ACRES) -0.644 **** -51.672 LOG(ACRES) -0.64071 **** -51.301LOG(EXP93) 0.388366 **** 5.08946 LOG(EXP93) 0.392701 **** 5.11721LOG(STRATIO) 0.123983 1.14411 LOG(STRATIO) 0.117159 1.06885FLOOD -0.18104 **** -3.9966 FLOOD -0.18411 **** -3.9848LOG(DENSITY) -0.01536 -0.7419 LOG(DENSITY) -0.01383 -0.6646ONMAJRD -0.12856 **** -4.2478 ONMAJRD -0.12787 **** -4.1796ONMINRD -0.1447 **** -4.8857 ONMINRD -0.14057 **** -4.729LOG(MEDHOUS) 0.191428 **** 3.96067 LOG(MEDHOUS) 0.203133 **** 4.11458LOG(PUBDIS) -0.0388 **** -3.3641 LOG(PUBDIS) -0.04062 **** -3.4631LOG(LTDRATE+0.5) -0.02227 -1.6992 LOG(LTDRATE+0.5) -0.02285 -1.7234LOG(SALE) 0.113676 **** 9.92005 LOG(SALE) 0.112507 **** 9.53051LOG(PORTCBD) -0.46965 **** -10.909 LOG(PORTCBD) -0.46907 **** -10.842LOG(BEAVCBD) 0.134891 **** 4.93595 LOG(BEAVCBD) 0.141465 **** 5.11254LOG(SEWERDIS) 0.00399 0.61222 LOG(SEWERDIS) 0.004855 0.74114HALFMI*BEFORE -0.05037 -0.4416 ONEMI*BEFORE 0.010551 0.2229HALFMI*AFTER 0.310008 **** 4.2769 ONEMI*AFTER 0.102248 **** 2.94546R-squared 0.72329 R-squared 0.721484Adjusted R-squared 0.720377 Adjusted R-squared 0.718552

Price Effects of AnnouncementLOG(EXP93) 0.388366 **** 5.08946 LOG(EXP93) 0.392701 **** 5.11721LOG(STRATIO) 0.123983 1.14411 LOG(STRATIO) 0.117159 1.06885FLOOD -0.18104 **** -3.9966 FLOOD -0.18411 **** -3.9848LOG(DENSITY) -0.01536 -0.7419 LOG(DENSITY) -0.01383 -0.6646ONMAJRD -0.12856 **** -4.2478 ONMAJRD -0.12787 **** -4.1796ONMINRD -0.1447 **** -4.8857 ONMINRD -0.14057 **** -4.729LOG(MEDHOUS) 0.191428 **** 3.96067 LOG(MEDHOUS) 0.203133 **** 4.11458LOG(PUBDIS) -0.0388 **** -3.3641 LOG(PUBDIS) -0.04062 **** -3.4631LOG(LTDRATE+0.5) -0.02227 -1.6992 LOG(LTDRATE+0.5) -0.02285 -1.7234LOG(SALE) 0.113676 **** 9.92005 LOG(SALE) 0.112507 **** 9.53051LOG(PORTCBD) -0.46965 **** -10.909 LOG(PORTCBD) -0.46907 **** -10.842LOG(BEAVCBD) 0.134891 **** 4.93595 LOG(BEAVCBD) 0.141465 **** 5.11254LOG(SEWERDIS) 0.00399 0.61222 LOG(SEWERDIS) 0.004855 0.74114HALFMI -0.05037 -0.4416 ONEMI 0.010551 0.2229HALFMI*AFTER 0.360377 ** 2.71399 ONEMI*AFTER 0.091697 1.64056R-squared 0.72329 R-squared 0.721484Adjusted R-squared 0.720377 Adjusted R-squared 0.718552

Conclusions

• Using data on vacant land sales in Washington County, Oregon, we examined whether the relationships between land values and proximity to planned station locations changed after the locations of stations were announced.

• We found that they did.

• Our empirical results add to the growing body of literature that suggests that plans to invest in transportation infrastructure can affect property values even before the infrastructure is in place.

Conclusions (cont.)

• Because we examined the impact of transportation plans on vacant land values, however, our results add more.

• First, our results suggest that information about light rail plans is observed by the development community, capitalized into land values, and is likely to alter the pattern of urban development in light rail station areas.

• Second our results provide support for a model in which planning is a behavior by a rational local government which can be used to alter urban development patterns for the purpose of increasing social welfare. This suggests plans indeed matter.

Hanley, Paul F. and Gerrit J. Knaap

The Spatial Reconfiguration of the Portland Metropolitan Area: A Preliminary

Assessment and Baseline Analysis

Proceedings of the Conference on Transportation, Land Use and Air Quality,

American Society of Civil Engineers, Portland, Oregon, 5/98.

Research Question

• Is development activity in Portland concentrating in various designated “centers”?

Research Strategy• Determine whether “centers” are attracting a

disproportionate share of building permits, improvement value, or sq.ft of improvements?

Portland’s 2040 Plan

City Centers

• City Centers contain the most intense forms of development and represent the focal point of commercial, residential and cultural activities.

Regional Centers

• Regional centers contain intense forms of commercial and governmental activities and should be well served by multiple modes of transportation.

Town Centers

• Town centers provide services to residents within a 2-3 mile radius,should have a strong sense of community identity, and a pedestrian friendly environment

Mainstreets

• Like Town Centers, Mainstreets have a commercial identity but should be pedestrian-friendly and strongly linked to adjacent neighborhoods

Neighborhoods

• Neighborhoods should be primarily residential but might contain locally serving commercial or community centers accessible by foot.

Research Design

• Measure share of various permits in various types of centers;

• Compare share of permits to expected share of permits; where

• Expected share of permits is based on distribution of permits across land use zones and distribution of zones across centers—assuming uniform spatial distribution

Conceptual Framework

Figure 1. Theoretical Pattern for building permit issuance

0.975

0.980

0.985

0.990

0.995

1.000

1.005

1.010

1990

1991

1992

1993

1994

1995

1996 .. .. .. .. ..

Year

Act

ua

l/B

ase

lin

e

Actual to Expected Percent Ratio of Building Permits in all Special Areas by Count, Value, and

Square Feet.

1990 1991 1992 1993 1994 1995 1996

count 0.84

0.84

0.50

0.54

0.40

0.65

0.68

value 1.21

0.57

0.48

0.72

0.52

0.70

1.31

sq ft 0.89

1.46

1.23

0.83

0.38

0.50

2.11

Ratio of Actual to Expected Percent of Building

Permits in all Special Areas by Permit Type. 1990 1991 1992 1993 1994 1995 1996

Commercial 2.032 1.580 0.973 1.514 1.791 1.211 4.542

Industrial 1.108 - 1.267 - 0.369 - -

Multi-Family 2.309 1.949 0.866 2.516 - 0.493 1.819

Single Family 0.645 0.790 0.409 0.365 0.341 0.621 0.675

Ratio of Actual to Expected Percent of Commercial

Permits by Special Area . 1990 1991 1992 1993 1994 1995 1996 Average

Main Streets 4.230 3.494 - 1.848 3.396 1.786 - 2.343

Regional Centers

- 0.744 0.698 2.359 3.372 1.520 - 1.739

Town Centers 4.450 1.225 1.534 1.080 2.911 1.879 18.788 4.552

Transportation Zones

0.967 1.731 0.750 1.690 1.467 1.021 - 1.271

Ratio of Actual to Expected Percent of Multi-Family

Permits by Special Area. 1990 1991 1992 1993 1994 1995 1996 Average

Main Streets - 9.697 - - - 0.866 2.263 1.565

Regional Centers

0.329 3.453 3.453 - - - - 2.412

Town Centers 3.896 1.411 0.940 1.118 - 0.567 3.950 2.259

Transportation Zones

0.122 0.961 0.855 3.725 - 0.558 1.795 1.176

Conclusion

• This examination of the pattern of building permits from 1990 to 1996 suggests that the pattern of permits is consistent with the plan.

• • A disproportionate share of commercial and multiple family

permits have been issued in areas designated as regional and town centers, main streets, and transportation zones.

• It is not clear that the plan caused this pattern of development or that the character of development is indeed transportation oriented.

• But the results are encouraging.

Conclusions (cont.)

• Perhaps more importantly, efforts to implement the plan are occurring coincidentally with efforts to monitor the implementation of the plan.

• The data and methods we use here to provide a preliminary assessment of the implementation of the 2040 plan are far from perfect.

• We hope, however, that data collection efforts like Metro’s and evaluation efforts like ours will stimulate further advancements in evaluating the implementation of plans.

Knaap, Gerrit J., Lew Hopkins, and Arun Pant

Does Planning Matter? Explorations into the Effects of Light Rail Plans on

Real Estate Values, Sales, and Building Permits

Lincoln Institute of Land Policy Working Paper, 1996

Research Question

• Are plans for light rail in Portland affecting patterns of development?

Research Strategy

• By displaying development events, use data visualization techniques to observe development patterns over time and space

Conclusions

• Dynamic data visualization proved to be an effective method of gaining new insights into the development process.

• Statistical analysis suggests that this is a relationship between the placement of a light rail line and development activity.

• The relationship, however, may be spurious as development patterns appear to be more profoundly influenced by larger scale factors.

Recommendations

• GIS data, GIS data, GIS data– Parcel level, regional, historical

• Think regional, act regional

• Exploit university resources

Additional References

• ECONorthwest and Johnson Gardner, Metro Urban Centers: An Evaluation of the Density of Development, 2001, Metro website.

• Bolen, Richard, Gerrit-Jan Knaap, and Ethan Seltzer, The Regional Land Information System: The Virtual Key to Portland’s Growth Management Success, working paper, Lincoln Institute of Land Policy, 2003.

• Ding, Chengri, Lewis Hopkins and Gerrit Knaap, Does Planning Matter? Visual Examination of Urban Development Events, Land Lines, 8,1, 1997