HOUSING & COMMUNITY DEVELOPMENT PLANNING

TOPICS

• ALLOCATION OF LAND• HOUSING • LOCATION, LOCATION AND LOCATION• REAL ESTATE ECONOMICS• NEIGHBORHOOD CHANGE• CLASS QUESTIONS

EL PASO'S LAND BY TYPE

71%

15%

14%

RESIDENTIAL

COMMERCIAL

INDUSTRIAL

Source: City of El Paso Planning Department

EL PASO'S LAND TOTAL

RESIDENTIAL

COMMERCIAL

INDUSTRIAL

OTHER

FRANKLIN

VACANT

Source: City of El Paso Planning Department

HOUSING AS A BUNDLE

ACCESSLOCATION

AMENITIES

LANDSOCIAL

NETWORK

ASSET

SHELTER

SCHOOLS

CREDIT

HOUSING

ALONSO’S MODEL OF URBAN LAND MARKETS

• Paradox: the poor live close to the center and the rich in the suburbs.

• There are two important attributes of buying land ( the land itself and location)

• The above paradox can be explained by the trade-off the household made between commuting (distance) and cheaper land

• Distance is a proxy to represent accessibility (to work) costs, then poor people tend to locate close to the center of the city.

THE REAL ESTATE MARKET

• Real estate as space (without a building) and as an asset (with a building)

• The difference between endogenous factors and exogenous factors. What kind of factor is planning?

• Housing is affected by micro forces (location specific) as well as macro forces (those that affect the profitability or use)

• Why are homes close to the mountain worth more than those away from the mountain?

VALUING LOCATION: Mountain View

eNDphcP phc

Sq feet constBedroomsBathroomsYear built

DDistance fromThe mountain

Value ofUndevelopedland

NNeighborhoodTASK scores

EP REAL ESTATE

Dependent Mth Rsq D.f. F Sigf b0 b1

VALUE LIN .615 44 70.27 .000 68529.2 3.0168

VALUE PROPERTY

SQ FEET

2000001000000-100000

700000

600000

500000

400000

300000

200000

100000

0

EP REAL ESTATE MARKET

Dependent Rsq d.f. F Sigf b0 b1

VALUE .710 44 107.48 .000 -32991 71.8823

VALUE PROPERTY

TOTAL LIVING AREA

800070006000500040003000200010000

500000

400000

300000

200000

100000

0

EP REAL ESTATE MARKETCoefficientsa

-30444.0 38989.283 -.781 .440402.122 388.677 .077 1.035 .30749.891 7.254 .585 6.878 .000

18051.798 16822.841 .150 1.073 .290-13441.5 14663.832 -.118 -.917 .365

1.866 .305 .485 6.127 .00018609.941 17298.867 .068 1.076 .289

(Constant)ageTotal living areaBATHSbedroomsSQFEETWEST

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: value proprertya.

Model Summary

.942a .887 .870 39646.89384Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), WEST, Total living area, age,SQFEET, bedrooms, BATHS

a.

EP REAL ESTATECoefficientsa

3.329 .772 4.311 .000-.010 .060 -.010 -.167 .868.756 .117 .572 6.447 .000.245 .072 .278 3.407 .002.263 .145 .166 1.812 .078.131 .106 .071 1.233 .225

(Constant)LNAGELNTLALNSQFLNBTHWEST

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: LNVALUEa.

Model Summary

.948a .899 .887 .24790Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), WEST, LNSQF, LNAGE, LNTLA,LNBTH

a.

SOME ELASTICITIESCoefficientsa

2.312 .599 3.860 .0001.210 .080 .916 15.107 .000

(Constant)LNTLA

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: LNVALUEa.

Coefficientsa

4.837 .709 6.825 .000.715 .078 .811 9.211 .000

(Constant)LNSQF

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: LNVALUEa.

Coefficientsa

12.441 .499 24.938 .000-.318 .141 -.323 -2.260 .029

(Constant)LNAGE

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: LNVALUEa.

HUD’S MODEL OF NEIGHBORHOOD CHANGE

STABLEvalues and incomes

In balanceDeclining: falling valuesImproving: rising values

1Healthy

2Incipient decline

3Clearlydeclining

4Acceleratingdecline

5Abandoned

NEIGHBORHOOD CHANGE

COLLECTIVE ACTION

INSTITUTIONAL ACTIONS

INDIVIUDAL ACTIONS