VACANT LAND I N F I L L
D E V E L O P M E N T
P O T E N T I A L
I N
P H I L A D E L P H I A
BRIAN TRAYLOR CAPSTONE PROJECT University of Pennsylvania Master of Urban Spatial Analytics Professors John Landis & Amy Hillier August, 2012
TABLE OF CONTENTS Executive Summary
4
Introduction
6
Urban Vacancy
6
Market-Driven Approach
8
Site Selection Model
9
AVAILABLE LAND
10
DESIRABLE LAND
12
PROFITABLE LAND
14
Composite Suitability Analysis
18
Results
20
Conclusions
23
Appendices & Bibliography
24
Executive Summary
This report summarizes the process and results of a comprehensive analysis of the city’s current vacant lot inventory of over 40,000 parcels. The results are conclusive that relatively few sites would be considered profitable for residential development by market and industry standards; but useful insights can be gleaned from the composite index that results in a rating for vacant sites that closely align with the market-driven factors. The model aims to provide each lot with its own “developability index” that can be compared to others within the neighborhood, or across the city. Individual property owners can begin to realize the potential value of their site, or come to terms with the fact that it is not appropriate for the development he or she was expecting. 4
De
le
Profita ble
By developing a site suitability analysis based on the categories of AVAILABILITY, DESIRABILITY, and PROFITABILITY a robust snapshot of developable land can be created. Within this context, potential investors can be targeted for areas of high probability of development, community groups can be activated to utilize a lot with no chance of future development, and neighborhoods at risk of losing land value can become the focus of municipal programs.
b is ra
ble ila
This study proposes to add value to all of these efforts by providing a useful and usable analysis based on current data and grounded by market-driven insight. Using the city’s current vacant land data and a layered site suitability analysis, this report will indicate which vacant lots are the best match for future residential development.
A va
Vacant land in Philadelphia is one of the most ongoing and unresolved urban issues facing the city. It costs the municipality millions in maintenance costs and uncollected tax revenue, and it represents a loss in billions to private property values in parts of the city already impacted by concentrations of poverty. Several initiatives have been developed to 1)mitigate the impact of vacant land on the city and its residents, 2) prevent more properties from becoming vacant, and 3) stimulate infill redevelopment on suitable sites.
The goal of this research was not to locate the next major redevelopment investment for the city of Philadelphia, but rather equip those who are doing community work in the city’s neighborhoods, and the city planners with the knowledge and perspective they need to be the most effective and impactful as possible. As a baseline, the results of this initial index can be compared to future analyses as market conditions change, or as Philadelphia continues to amend its zoning code by planning district. The model of analysis as market-driven and based in real data serves as a tool, not just to planners, and community leaders, but also to developers who must be accountable to investors before they move forward. The spaces where all of the factors of available, desirable and profitable overlap should be the focus of future redevelopment efforts in Philadelphia
Of all the vacant sites analyzed in this report, only 92 resulted in a positive land value per square foot of residential development. These sites were concentrated around the periphery of Center City Philadelphia, extending northeast toward Northern Liberties, and South of Old City. These sites hold the highest potential for future development based on all three criteria of the site suitability analysis.
On the other end of the development spectrum are the vacant lots that are too small, or have too many cost restrictions to be considered profitable for residential development. Although some remain on the fringe, others will never attract private sector funds that must meet a standard return on investment before any redevelopment efforts begin. These areas are concentrated in the neighborhoods of southwest Philadelphia, and Grays Ferry.
5
Introduction The city of Philadelphia has experienced a significant loss in population over the second half of the twentieth century; decreasing from a total of over 2 million residents at its peak in 1950 to only 1.56 million as of the 2010 U.S. Census. This reduction in population has not only presented economic challenges that test the city’s capacity to sustain its’ existing infrastructure with a decreasing tax base, but also environmental effects on Philadelphia’s neighborhoods and a social impact to its’ communities. The loss in population is most noticeable to residents in the form of vacant and abandoned buildings and lots scattered throughout the city, particularly in residential neighborhoods where they pose threats to safety and hold the potential to diminish property values.
Urban Vacancy Vacant land is distributed throughout Philadelphia, but primarily concentrated in the residential neighborhoods in the north and west of the city. Although the high maintenance costs and lost value of uncollected taxes are shared by the entire city, the local effects of blight on property value and neighborhood perception disproportionately impact these areas. According to the city of Philadelphia’s current property data, there are over 44,000 vacant parcels covering an area of 4,644 acres in the city. This is under utilized land is three times the area of Center City Philadelphia. The Redevelopment Authority of the City of Philadelphia (RDA) reports that vacant land accounts for $3.6 billion in lost household wealth, over $20 million in city maintenance cost each year, and at least $2 million in uncollected property taxes each year.
Citywide Vacant Land Area is 3x the Area of Center City
6
7%
RDA
1.7%
PHDC
14.3%
Public
77% Private
Vacant Land Ownership
Vacant land management has been an ongoing priority of the city of Philadelphia, as well as several local and national agencies that work toward the revitalization of urban areas. Some approaches are focused on creating a more productive landscape than the existing fallow lots; proposing a variety of vegetative and park-type uses as temporary and immediate solutions to a more permanent and eventual infill redevelopment. Other proposals have included land banks, and city-sponsored redevelopment efforts. These approaches fail to address an underlying issue regarding vacant land in Philadelphia, particularly relative to ownership. Over three quarters of the vacant land in Philadelphia is privately owned and subject to the risks associated with a constantly shifting real estate market. Although small scale, grass roots neighborhood efforts may prevent the negative impacts of the blight associated with vacant parcels, a more comprehensive approach to vacant land management and eventual infill development must be oriented toward a market-driven audience. According to a National Vacant Properties Campaign report with recommendations to the city, vacant land in Philadelphia can be regarded as a “reservoir of potential revitalization assets,� provided that a comprehensive systematic approach is undertaken to address the issues of fragmentation and reduce obstacles to private investment.
Vacant Land in Philadelphia
In some neighborhoods, entire blocks have been abandoned, leaving only vacant lots and deteriorating buildings.
Source: City of Philadelphia 2012
7
Market Driven Approach A comprehensive and sustainable vacant land strategy must be grounded in the reality of an urban real estate market if it going to move beyond community gardens or using vacant lots for art exhibitions. Although these efforts have their merits, and they are most often the first response to deal with urban vacancy, cities such as Philadelphia with decades of vacancy and a growing vacant land area need to move beyond the site specific interventions and move to a more global and market-driven approach. Every vacant lot within the city presents an opportunity for development, but if there was a potential profit to be had, chances are a developer would have moved forward on that opportunity. In a market such as Philadelphia, with so many vacant parcels with uncertain development prospects, it is difficult for a private investor to justify conducting an independent profitability analysis any given property without the expectation of recouping that cost. That is where a market-driven approach to conduct a city-wide analysis as the first step that the private sector would take if they were to propose development. Before any redevelopment effort begins, a series of basic calculations are conducted to determine if a potential profit is possible (and likely) given a series of estimated costs and expected revenues. Although these calculations are based on many assumptions, the cursory analysis is invaluable to the development process, and immediately sets a line where development is a possibility, or it simply is not. As factors fluctuate and rates go up and down, the line may change, but the question remains as to where that line occurs. Where the go or no-go decision is made regarding the potential infill of a currently vacant parcel is where this analysis will focus. In order for developers to feel comfortable moving forward with what will be a multiyear (and in many cases multi-million dollar) planning, design and construction effort, there are several factors that must be addressed, but the bottom line is profitability. In the 8
Community Garden, Philadelphia, PA
Vacant Lots Used for Art: “El Camino� by Cessna Decosimo (Chattanooga, TN)
Site Selection Model
De
ble a r si
le Currently vacant Residentially zoned Large enough to allow residential uses Outside of flood hazard areas
Accessible to transit Close to parks or open space In a nice community with good schools Close to employment opportunities
Pro
Revenue > Costs
fitable
Analyzing vacant parcels for development profitability takes away the first obstacle to redevelopment that potential developers face, and adds value to the city’s existing data. On one side, the results of a citywide profitability analysis can be used by the private sector in order to narrow their focus on likely successful redevelopment opportunities, but also at the other end where the city and other agencies can focus their efforts because the private sector will have little interest in improvement or investments. The real value of conducting this marketdriven analysis at a citywide scale is at the fringe of profitability, where only a few percentage points mean the difference between a great opportunity being left vacant, and Philadelphia’s next successful redevelopment effort.
ab
private sector, where a majority of the vacant lots are held, developers must be accountable to investors in order to move forward with any infill redevelopment plans.
Av ai l
Analysis Proposal: Use the city of Philadelphia’s existing parcel data to determine which vacant sites are most likely to be profitable for residential development. The analysis will focus on residential properties to maintain consistent cost and revenue comparisons of multifamily development of rental properties. In an effort to provide realistic and robust results that can actually be used by potential investors, a site suitability analysis approach will be used that take into consideration three main factors; vacant land that is AVAILABLE, DESIRABLE, and PROFITABLE. Although there is no shortage of vacant land in Philadelphia, it can be assumed that many of the lots available for development are not suitable for development; either because of existing regulatory restrictions, environmental limitations or accessibility, in addition to their capacity to yield reasonable profits significant enough to offset a substantial investment. 9
Available Land: Environmental Preconditions
Nonresidential Zoning
Vacant Parcels: 44,016 Total Acres: 4,644.51 Average Parcel Size: 4,596.39 sf
10
The city of Philadelphia covers a large land area of about 135 square miles, but a significant portion of that area is reserved for non-residential uses. Industrial and commercial zoned properties account for a large portion of the city’s vacant land area, particularly in South Philadelphia, and areas along the rivers. Residential development is not completely prohibited in all of these areas, but for the purposes of maintaining a conservative profitability model, this analysis will only look at vacant parcels with existing residential zoning. See the Residential Development Profitability Model and the Zoning Appendix to see how these properties were eliminated.
Flood Hazard Zone Areas
Remaining Vacant Parcels:
= Not all of Philadelphia’s currently designated vacant parcels are actually suitable for any proposed development, let alone profitable residential development. The Flood Hazard Zone areas include the city’s waterways, and adjacent areas susceptible to flooding. The data for these areas comes from the Flood Insurance Rate Maps (FIRM) maintained by the Federal Emergency Management Administration (FEMA). Residents within the 100 year floodplain are required to purchase flood insurance. For the purposes of this analysis, the vacant properties within the 100 and 500 year floodplains are taken out of the equation.
Based on the first criteria of available land, the total remaining vacant parcels suitable for further analysis is limited to 7820 parcels, covering an area of 1,248 acres.
11
Desirable Land: Neighborhood Assets 10
10
Distance from Transit:
0
5
Miles 10
10
Distance from Parks:
0
+ Philadelphia has a regional public transportation system that extends beyond the central core into surrounding suburban communities and job centers throughout Southeastern Pennsylvania and New Jersey. Access to this system is an asset that residents seek when making decisions about where to live. In an urban environment like Philadelphia, access to transit is one of the most important desirability factors. For this analysis, SEPTA’s high speed stations along the Broad Street and Market Frankford Line are used as centers of a Euclidean Distance that extends to the city limits. Areas closest to the stops rate higher on a scale of 1 to 10.
12
0
Elementary School Quality
0
+ The park system in Philadelphia is one of the city’s greatest assets. With over 9,000 acres of parks, recreation areas, and open space, Fairmount Park is one of the nations largest urban park systems. However, many parts of the city do not have access to park space, putting them at a disadvantage, and less desirable than other parts of the city. Like distance from transit, the desirability of an area decreases as the Euclidean distance from a park increases. This analysis included a 1 mile limit on the desirability factor for distance from parks, as the value of the space is may not be as directly realized as transit beyond one mile.
+ A major decision factor for many families choosing where to live is the quality of the schools their children will have access to. Philadelphia’s public education system experiences many of the challenges that face urban school districts including limited funding and retaining quality teachers. The Pennsylvania State Department of Education administers an assessment (the PSSA) to all students to determine if specific schools are maintaining an adequate yearly progress (AYP) toward standard levels of proficiency in reading, math and science. This analysis uses AYP to rate desirability on a scale of 1 to 10 for all schools in the city.
10
Composite Desirability Index
Jobs & Higher Education
Producing a raster-grid for the entire city allows for a gradient of desirability to be measured across these individual factors, each of which is calibrated to the same scale of 1 to 10
0
= Universities in Philadelphia, and their affiliated hospital systems are the largest employers in the city. Neighborhoods surrounding these institutions are highly desired in the residential market for employees and students, and often maintain a solid mix of amenities to market to potential tenants, including retail, restaurants, and commercial uses. This analysis includes a Euclidean distance spectrum for the highest desirability closest to a university and moving toward 1 for areas farthest away within the city.
Combining all of these factors into a single city-wide grid yields a desirability index between 5 and 38, with no area of the city having all amenities, but also no area lacking all of them.
38 5
13
Profitable Land: Value Per Square Foot Estimating the profitability of multifamily residential development in a city like Philadelphia is an exercise in speculation. There are countless factors that impact the bottom line of investment, so it is impossible to determine a definitive value per square foot. Some of these factors may be partially controllable (design and construction costs) but most are not (vacancy rates and the rental market). It is possible within this context to establish a standard based on a few assumptions, market data and underlying demographic indicators. This proposed model takes each of the vacant parcels that are available for development (zoned residential, large enough, and outside of a flood zone) and subjects them to the following residual land valuation. The detailed results of the model are included in the Residual Land Appraisal Appendix
14
Permitted Development
Development Costs
This includes the number of residential units permitted by the zoning code, either regulated by an allowable floorarea ratio (FAR), or height limit and lot coverage percentages. This figure also accounts for common square footage, and the costs of structured parking in the cases where parking is required.
This figure includes an overall price per square foot for construction of $150, and an estimated cost of $22,500 per structured parking space. Included in the development cost model is a soft cost ratio of 35% to account for non-construction related expenses associated with designs, permitting and fees.
Net Operating Income The costs to operate and maintain a residential property can range depending on its scale, age and occupancy levels, but the standard assumption for anticipating operating expenses for new construction is 20%. In this analysis the overall net operating income is derived from the gross scheduled rent, based on each parcel’s respective census tract Median Contract Rent, with a 150% multiplier for a new-construction factor. A percentage of the scheduled rent is taken out to account for vacancies, and that number is based on each parcel’s respective census tract Rental Vacancy Rate.
Low
(
High
NET OPERATING INCOME
Capitalization Rate Determined by dividing unoccupied and unrented households by the total of all rental units (unoccupied, occupied, and rented) Data from 2010 ACS 5-Year Estimates. Miles 10
5
0
Median Contract Rent: Low
High
The capitalization rate is the rate of return on a real estate investment property based on the expected income that the property will generate. This figure would be used to estimate the potential return on an investment by dividing the income a property will generate by the total value of that property. In the case of this residual land appraisal model, a market capitalization rate of 7.5 is used based on a 2010 Real Estate Research Corporation estimate of 6.9 for first tier newly constructed properties in prime or good locations in Philadelphia. The national average for apartments was 7.5.
BY
DIVIDED
CAPITALIZATION RATE
=
(
Rental Vacancy Rates:
EQUALS
ESTIMATED VALUE MINUS
TOTAL DEVELOPMENT COST
= EQUALS
RESIDUAL LAND VALUE Data from 2010 ACS 5-Year Estimates. A citywide mean is used for census tracts with insufficient data for rental rates.
15
Residential Development Residual Land Value Running the residual land appraisal model on the AVAILABLE vacant parcels produced a new data set for the city. These properties have a market-driven property value estimate that can be used to more accurately assess the impact of lost value. Assuming multi-family residential development across all parcels produced a wide range in land values, the majority of which were negative.
Calculated Land Value -$181,246,178.00- -$1,426,272.10 -$1,426,272.10 - -$816,581.71 -$816,581.71 - -$630,650.22 -$630,650.22 - -$533,604.52 -$533,604.52 - -$482,524.41 -$482,524.41 - -$439,754.72 -$439,754.72 - -$405,515.99 -$405,515.99 - -$367,831.96 -$367831.96 - -$311692.72 -$311,692.72 - $284,5771.74
Because each property was subject to most of the same constants, (particularly within any given census tract) the calculated land values are not as meaningful as their location within the range. Changinng any one factor may move the profitability line enough to change entire neighborhood outcomes
16
Land Value/SF
Concentrations of Likely Residential Development
A more accurate account of the results of the appraisal model is the value of land calculated per square foot for each vacant lot. Although most of the city’s larger parcels were removed from the analysis because of their zoning, or proximity to floodplains, there is still significant variety in the scale of these parcels.
A citywide aggregation of the land value/sf shows which neighborhoods have higher concentrations of vacant parcels with estimated positive value. It is clear that neighborhoods in Center City, Northeast Philadelphia and the Chestnut Hill/ Roxborough area have the highest valued vacant land in Philadelphia
The Land Value per Square Foot will be used as the PROFITABLE indicator in the final composite analysis along with the DESIRABLE indicator from the proximity raster analysis. The inset neighborhood map below shows less variety in value than the gross calculated land as a result of aggregating the values into ten quantiles to correspond with the environmental analysis and produce a single summary suitability map.
Somerton Byberry Bustleton West Torresdale
Pennypack Park Chestnut Hill
Cedarbrook
Fox Chase
West Oak Lane East Mount Airy
Rhawnhurst
Pennypack
West Mount Airy East Oak Lane Oxford Circle Roxborough East Germantown Mayfair Lawncrest Ogontz Olney Germantown Logan Tacony Manayunk Juniata Park Frankford East Falls Tioga Hunting Park Allegheny West Wynnefield Overbrook Haddington
Mill Creek Powelton Cobbs Creek Cedar Park Kingsessing
Elmwood
Harrowgate
Bridesburg Strawberry Mansion Hartranft Richmond North Central Kensington Brewerytown Fairhill
Holmesburg
Fishtown
_ -$444.30 - -$285.02 -$285.02 - -$268.84
Center City
Schuylkill Point Breeze Grays ayss F a Ferry
Riverfront Riivverfront
Pennsport South Philadelphia
-$268.84 - -$248.32 -$248.32 - -$231.52 -$231.52 - -$215.90 -$215.90 - -$199.00 -$199.00 - -$174.68 -$174.68 - -$141.45
Girard Estates
-$141.45 - -$98.41 -$98.41 - $123.90
Eastwick
17
Indexed Value/SF
Indexed Desirability Factor
+
18
10
10
0
0
Likely Sites for Residential Development
=
The final step in this composite analysis is to overlay the results of the DESIRABILITY analysis with the PROFITABILITY of the residual land value model results to produce a final overall map of likely sites for residential development. In this case, the desirability factor between 1-10 matches the scale of price per square foot indexed between 1-10, but this model can be adjusted to weigh either set of factors more heavily. Combining these two sets of data holds the potential to produce a variety of numeric outcomes, making it difficult to determine which sites may be more profitable, but less desirable or vice versa. In this instance however, only the top quantile of profitable sites were positive, making them easy to isolate. The highest resulting parcel composite index was 20, and the lowest was 3. In this case, even more than the residual analysis alone, it is more relevant where on the spectrum an particular vacant parcel falls than its individual index value. If the analysis were to be adjusted based on an increase to one of the citywide constant variables such as construction cost or capitalization rate, the line at which these vacant parcels become profitable may move from 20 to 19, or 18. It is highly unlikely however, that based on this method that any of the properties indexed below 10 or 15 would ever come out to be positive, unless they are subject to a significant change in zoning. 19
Highest Score
EY
LEMON
GALLOW A
06TH
RE NO
17TH
CLAY
IN
CALLOW HILL CARLTO N
W TO N
WILLOW
VINE STRE
VINE
SOUT
H PEN
N CLOVER
STOCK EX CH
ANGE
MORAVIA
INDEPE NDENCE
FILBERT COMMER CE
LUDLOW
ELFRETHS
14 CHURCH
EAD
15
AMES
LEITHGOW
LAT ONA
AMERIC
E
MA
INGT ON
US BLVD
QUEE N BEC K
COLUMB
N
WAS H
FMAN
BODINE
KAUF
HANCO CK
4TH
MONRO
ORIANN A
MARSH ALL 6TH
E
RANDOLP H
ROS
20
STAMPE RS
PHILIP
TITAN
ANNIN
SAL TER MON T
18 19
DELANC EY
AN
5TH
REESE
PERT
H
NSON HUTCHI
ALDER
CAR HALL PEN TER
ERN ST
1TH
KENILW ORTH BER TON
SAINT ALBAN S
RANKLI
HICKS
MARSHAL LS
FULT ON
LEA GUE
CHADW
ICK
ALL
20
S
SCHELL
DELHI
QUINCE JESSUP
KIMB
ALTER
WILLING
PEM CATHAR INE
16 17
JAMES
ADDISON
N MILDRED 8TH
ER
CLINTON
DARIE
WEBST
CLIFTON
ROS
EWO
OD
FAWN
PANAMA
SARTAIN
SMEDLE Y
PINE
MANNING
CYPRES S
MARVINE
WAVERLY
16TH
SAINT
SAINT PETERS
13TH
LATIMER CAMAC
BACH
CK DO
SYDENH AM
Y
LETITIA
BANK
IONIC
SCHUBE RT
13
LUDLOW
RANST
DRURY
N
11 12
BREAD
QUARRY
RACE
WATE R
NEW
BENJAMIN FRANKLIN
REE
T ST
T
CHERRY
10
MARKE
FLORIS
LETITIA
SPRING
YORK
5TH
ET EXP WINTER
APPLET
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BEA
EX P
9
PEARL
REET
SUMMER
7 8
VINE ST MOLE HICKS
AN G LO
BODINE
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WOOD
4
6 DE LA WA RE
IN
KL
AN
FR
SHAMOK
NECTARINE
3
5
3RD
BUTTON WOOD HAMILTO N
LE RC CI
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9TH
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EL
GREEN 12TH
BUTTONW
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MINTZER
MONTER
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WAR NOC K
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NORTH
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PARK
As a result of the composite analysis, the highest scores reached a perfect 20, and are clustered around the periphery of Center City Philadelphia. Although this provides little additional insight than what may be considered common sense, it is important to note that expected results indicate the validity of the model.
Second Highest Score ORP PALETH
W LEITHGO GALLOW AY
FLORIST
CHERRY
REE
SOUTH
PENN CLOVER
ANGE
MORAVIA
INDEPE NDENCE
FILBERT COMMER CE
RANSTE AD CHESTN UT IONIC
LUDLOW
W LEITHGO
LEE
DUNTON
STAMPE RS
IN
AMERICA
QUEE N BEC K
INGT ON
COLUMB
WAS H
HANCOC K
MAN
BODINE
RANDOL PH
MARSHA LL 6TH
KAUFF
US BLVD
4TH
MONROE
PHILIP
L TITAN ATONA
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FRANKL
ANNIN
ROS
19
N
5TH
REESE
PERT
H
SON HUTCHIN
ERN ST
SAL TER MON T
MILDRED 8TH
CLIFTON
FAWN
DELHI
QUINCE
ALDER
CAR HALL PEN TER
18
20
DELANC EY
FULT ON
LEA GUE
11TH
KENILW ORTH BER TON
SAINT ALBANS
CATHAR INE
ALTER
HICKS
MARSHA LLS
ADDISON
DARIEN
MANTON
MED
N
PERCY
INA EARP
GERRIT
T WILDER
D
SEARS REED RD
COLORA DO
CLINTON
WILLINGS
PEM
LL
CHADWI CK
BOUVIER
R
KIMBA
CARLISL E
19TH
OPAL GARNET
DORRAN CE
18TH
WOODST OCK
EAGUE
WEBSTE
MARVINE SARTAIN
OD ROS
EWO
16TH CLEVEL AND
FULTON
HARSHA W
SENATE
CLYMER
MANNING
CYPRES S
PANAMA
17
JAMES
SCHELL
CAMAC
PINE
JESSUP
WAVERL Y
KATER
SAINT
SAINT PETERS
13TH
SYDENH AM
LATIMER
SMEDLE Y
RODMAN
UBER
21ST
TRYON
BACH
CK DO
DELANC EY PANAMA
LETITIA
BANK
LT VAN PE
MANNING
BEA CH
ST
AD
IONIC
ES SCHUBE RT
16
CHURCH
SAINT JAM
14 15
ELFRETHS
LUDLOW
RANSTE
DRURY
N
R
RACE BREAD
QUARRY
STOCK EX CH
CAPITO L
EX P
BODINE
APPLET
COMMERCE
CYPRES S
10
13 NEW
BENJAMIN FRANKLIN
WATER
SPRING
05TH
VINE
T EXP WINTER
LETITIA
VINE STREE
MARKET
REE
ELLO
9
12
PEARL
APPLET
HANC
8 EL LE N
REET
SUMMER
WALDEN OHN F KE NNEDY
ORY
7
11
VINE ST MOLE HICKS
EY
ONSALL
WILLOW
YORK
CROSK
LE RC CI
CARLTO N
N
AN G LO
W TO
SPRING
WOOD
HILL
CA NA L
6TH
HOFFMAN
NOBLE CALLOW
IN
WINTER
NECTARINE
DE LA WA RE
IN
KL
AN
SHAMOK
3RD
BUTTON WOOD HAMILT ON
NE
FR
TO W
ORIANN A
K
9TH
PA R
EL LE N
GREEN 12TH
N
17TH
WOOD
LAUREL
RE NO
CLAY
BUTTON
ME
POLL ARD
PE NN
EY
LEMON
6
WILDEY
MINTZER
MONTER
PAR R
DARIEN
WAR NOC K
PARK
CAMAC
WATTS
WILCOX
GEORGE
ISH
CLAY
5
SA
NORTH
RANDOLPH
MARSHALL
PERCY
E
BURNS
OPAL UBER
TAYLOR
WOODST OCK
CAPITOL
CORINTHIAN
WN
MA
BRO
IL
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NE
EN
4
O
D EL FI ND LA LE
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CAMBRIDGE
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HARPER
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M
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MYRTLE PARRISH
ON
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HARPER GEORGE
POPLAR
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MASTER E CR
GIRARD
CLEVEL AND
THOMPS
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JEFFERSON
KINGS
SMEDL
CABOT
DER
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CA
The sites that achieved a score of 19 out of possible 20 are still within the realm of potential development, although none of them will have an individual residual value appraisal greater than zero. Only the top quantile of land values per square foot had a positive land value based on the profitability model, so only the perfect score holds that potential. For these parcels, if a different capitalization rate, or construction rate is used, there is a distinct possibility that the model will yield these sites as profitable in the future. Additionally, with the American Community Survey estimates being updated each year, new rental and vacancy rates could move these vacant parcels from the cusp of profitability to valuable assets and likely sites for residential development.
21
R
ON TI EN
E
55TH
TANEY 30T
27TH
STAN LEY
29TH
PATTON
R
28TH
NEWKIRK
RT R
IDE
N
MARSTO N ETTING
MIFFLIN
HOFFMAN
EN D AL
BA
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MA
13
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ND 72
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76
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33R D
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GRO VE HAR MON Y
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58
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NV
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CHRISTIA
MONTROS
NW
HAMILTO
LARCHWO
CO
OSAGE
CEDAR
WEBSTER
NORFOLK
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SA
HAZEL
PEMBER TON WALTON CATHARIN E
COBBS
The sites with the lowest scores represent vacant parcels that are not suited for multifamily residential development. These parcels are concentrated in southwest Philadelphia and the Grays Ferry Neighborhood. In more site specific circumstances, many of these parcels would be eliminated from consideration, but they offer important insights regarding the future of sites that will never be profitable for a variety of potential land uses. These should be areas of critical concern to the city and organizations working toward revitalization if there is any expectation that eventual market-driven residential infill will reach these sites.
36T
Lowest Scores
RO EN
SE
Conclusions Following the three tiered approach to conduct a comprehensive analysis of Philadelphia’s vacant lots provides an understandable and usable market-driven index of likely development sites. As may be expected, the most valuable parcels of land with the highest desirability are located adjacent to high quality neighborhoods with stronger residential housing markets. This is a testament to the validity of the valuation model, and also an indicator of the reliability of the model at the lower end of the spectrum. As much can be gleaned from the lowest valued sites as the highest in terms of the city’s priorities and where alternatives to infill redevelopment may be explored. Through this research, it several concepts regarding vacant land and the potential for infill redevelopment became clear. •
Zoning Does Matter: Outside of the citywide constant market assumptions and variability of vacancy and rental rates across census tracts, the most significant determinant to the potential profitability of a site is its zoning regulations. Many residential districts do not require any parking, reducing the construction costs considerably, and taking away valuable land area. This analysis also utilized the city’s newly adopted zoning categories, making the application of standards much simpler than the existing code would have allowed. See the Zoning and Residual Appraisal Appendices for the conversions.
•
Construction Costs Matter: Initial analyses used a much higher construction cost rate of over $200 per square foot, resulting in no vacant lots being profitable. Further research indicated that although Philadelphia has higher construction costs than other areas, an accurate figure is closer to $150 - $180 per square foot.
•
Residential Development is a Major Investment: The estimates produced by the residual land appraisal model represented a substantial level of investment. Collectively, billions would need to be invested in the highest rated parcels alone to produce minimal profits. This underscores the value of research being done in the area of vacant land management and urban infill development, as well as the need for more temporary solutions that continue to mitigate the impacts of these properties on their neighbors and the city.
•
Aggregating Parcels Makes Sense: Due to the minimum size requirements for development in each residential zoning category, several thousand vacant parcels were eliminated from the analysis. It becomes clear why land banking and redevelopment efforts work toward acquiring contiguous land. Although it would be possible to aggregate adjacent parcels in this analysis and run the model considering clusters as one property, that would not produce an accurate index for each parcel independently as was the goal of this process.
23
ZONING APPENDIX PHILADELPHIA’S NEW RESIDENTIAL ZONING CATEGORIES: RSD, Residential Single-Family Detached Districts The RSD, Residential Single-Family Detached districts are primarily intended to accommodate detached houses on individual lots. It is intended that RSD zoning be applied in areas where the land use pattern is characterized predominately by detached houses on individual lots or where such a land use pattern is desired in the future. The Zoning Code includes three RSD districts that are differentiated primarily on the basis of minimum lot area requirements. RSA, Residential Single-Family Attached Districts The RSA, Residential Single-Family Attached districts are primarily intended to accommodate attached and semi-detached houses on individual lots, but may be applied in areas characterized by a mix of housing types, including detached houses. The districts are also intended to provide a density transition between RSD districts and RM districts. The Zoning Code includes five RSA districts that are differentiated primarily on the basis of minimum lot area requirements. RTA, Residential Two-Family Attached Districts The RTA, Residential Two-Family Attached districts are primarily intended to accommodate two-family, semi-detached houses on individual lots, but may be applied in areas with a mix of housing types , including detached and attached houses. RM, Residential Multi-Family Districts The RM, Residential Multi-Family districts are primarily intended to accommodate moderate- to high-density, multi-unit residential buildings in areas where such development already exists or where it is desired in the future. The Zoning Code includes four RM districts. These districts are differentiated primarily on the basis of allowed minimum lot area per unit and allowed building heights. RMX, Residential Mixed-Use Districts The RMX, Residential Mixed-Use districts are intended to accommodate residential and mixed- use development. The RMX-1 district is further intended to promote conservation of existing topography, trees, natural waterways, and other natural resources, as well as preservation of historically significant buildings, structures, and property. The RMX-3 district is intended for application primarily in Center City.
24
DISTRICT PERMITTED USES SINGLE FAMILY DUPLEX / TWO FAMILY MULTI-FAMILY RESIDENTIAL RELATED USES NON-RESIDENTIAL USES OTHER PERMITTED USES PERMITTED BUILDING TYPE DETACHED SEMI-DETACHED ATTACHED MULTIPLE BUILDINGS / LOT ZONING REQUIREMENTS MINIMUM LOT WIDTH (FT.) MINIMUM LOT AREA (SQ. FT.) MINIMUM OPEN AREA (%) OF LOT SET-BACK LINE / FRONT YARD MINIMUM DEPTH (FT.) SIDE YARD MINIMUM WIDTH:
R-1
R-1A
R-2
R-3
R-4
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
75
65
50
50
35
10,000
7,800
5,000
5,000
3,150
65
65
70
70
60
35
35
25
25
15
2 @ 15
2@ 10
Detached single family dwelling – corner lot (ft.) Semi-detached single family dwelling (ft.) Attached single family dwelling (ft.) Detached duplex dwelling (ft.) Detached duplex dwelling – corner lot (ft.) Semi-detached duplex dwelling (ft.) Attached duplex dwelling (ft.) Multiple dwelling (ft.) Building other than dwellings (ft.) Building other than dwelling, corner lot (ft.) REAR YARD MINIMUM DEPTH (FT.) REAR YARD MINIMUM AREA (SQ. FT.) BUILDING HEIGHT LIMIT – DWELLING (FT.)
15
10
BUILDING HEIGHT LIMIT – NON-DWELLING (FT.)
Detached single family dwelling (ft.)72 73
MAXIMUM NO. OF STORIES – DWELLING OFF-STREET PARKING
2 at 25 total, each 2 at 25 total, each not <10 not <10 7
Residential Unit Count Process For Residential Districts Without FAR 1. Eliminate all lots that do not meet the minimum lot area requirement. 2. Determine the difference between the open space % required and the total lot size to get the square footage of a residential floor plate. 3. Multiply the number of stories allowed by the square footage of the floor plate to get the total square footage of the structure permitted. 4. Divide the total square footage by the average 1000 sf unit size to determine the number of units.
2@ 8
7
6
25
16
2 @ 15
2 @ 15
2 @ 10
15
15
10
25
25
20
30
30
35
35
35
35
35
35
35
35
35 + add’l height 1
35 + add’l height 1
3
3
3
3
3
See C hapter 14-1400 of this Title.
RESIDENTIAL PARKING REQUIREMENTS Minimum Required Parking Spaces (spaces per unit/sq. ft. of gross floor area/beds/seats) RSD-1/2/3 RSA-1/2/3 RTA-1 RMX-1
RSA-4/5 RM-1
Single-Family
1/unit
0
0
Two-Family
1/unit
0
1/2 units
RM-2/3/4 RMX-2/3
Residential Use Category (as noted below) Household Living (as noted below)
Multi-Family
1/unit
0
3/10 units
Group Living (except as noted below)
1/10 permanent beds
1/10 permanent beds
1/10 permanent beds
Single-Room Residence
1/20 units + 1; min. 2
1/20 units + 1; min. 2
1/20 units + 1; min. 2
25
RESIDUAL LAND APPRAISAL APPENDIX Parcel _ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 18 19 20 21 22 23 24 25 26 27 28
SHAPE_AREA 3251.832038 11277.962488 2376.594543 1525.122672 1490.573277 1796.875205 2545.418357 2271.994325 1759.085528 2317.119673 1618.832657 1466.700923 2254.230531 3793.627787 2203.960815 2013.032382 1615.723516 1836.894572 1796.650534 1682.19585 1907.113912 1650.994507 1633.363491 5414.331083 2191.859501 2911.949343 7277.35122
SHAPE_LEN 362.208003 458.57901 296.263966 201.168736 191.517429 212.827716 220.053505 217.703805 219.442427 226.418728 196.749538 185.353761 200.790384 310.939148 262.509154 260.702592 231.422564 257.205468 256.208433 251.918791 269.775356 234.320158 233.74933 360.423074 262.474205 261.393706 348.125019
NAMELSAD10 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 366 Census Tract 38 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 83.02 Census Tract 176.02 Census Tract 176.02
TotalHousi 1350 1350 1350 1350 1350 1350 1350 1350 1350 1350 1350 1350 1350 1723 2134 2134 2134 2134 2134 2134 2134 2134 2134 2134 2134 1371 1371
RenterOccu 431 431 431 431 431 431 431 431 431 431 431 431 431 310 766 766 766 766 766 766 766 766 766 766 766 637 637
Unrented_V 101 101 101 101 101 101 101 101 101 101 101 101 101 21 56 56 56 56 56 56 56 56 56 56 56 91 91
Rented_Vac 3 3 3 3 3 3 3 3 3 3 3 3 3 0 4 4 4 4 4 4 4 4 4 4 4 3 3
TOT_RENT 535 535 535 535 535 535 535 535 535 535 535 535 535 331 826 826 826 826 826 826 826 826 826 826 826 731 731
RentalVaca 0.188785 0.188785 0.188785 0.188785 0.188785 0.188785 0.188785 0.188785 0.188785 0.188785 0.188785 0.188785 0.188785 0.063444 0.067797 0.067797 0.067797 0.067797 0.067797 0.067797 0.067797 0.067797 0.067797 0.067797 0.067797 0.124487 0.124487
MED_RENT 1724 1724 1724 1724 1724 1724 1724 1724 1724 1724 1724 1724 1724 711 606 606 606 606 606 606 606 606 606 606 606 558 558
ADJ_RENT =K2*1.5 =K3*1.5 =K4*1.5 =K5*1.5 =K6*1.5 =K7*1.5 =K8*1.5 =K9*1.5 =K10*1.5 =K11*1.5 =K12*1.5 =K13*1.5 =K14*1.5 =K15*1.5 =K16*1.5 =K17*1.5 =K18*1.5 =K19*1.5 =K20*1.5 =K21*1.5 =K22*1.5 =K23*1.5 =K24*1.5 =K25*1.5 =K26*1.5 =K27*1.5 =K28*1.5
Residual Land Appraisal Formula: Based on Professor John Landis’
26
CODE R10 R10 R10 R10A R10 R10A R10A R10A R10A R10A R10A R10A R10A R5 R9 R9 R9 R9 R9 R9 R9 R9 R9 R9 R9 R9 R5
New_Code RMͲ1 RMͲ1 RMͲ1 RSAͲ5 RMͲ1 RSAͲ5 RSAͲ5 RSAͲ5 RSAͲ5 RSAͲ5 RSAͲ5 RSAͲ5 RSAͲ5 RSAͲ3 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RMͲ1 RSAͲ3
PARKING 0 0 0 0 0 0 0 0 0 0 0 0 0 =V15 0 0 0 0 0 0 0 0 0 0 0 0 =V28
Stories 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
OpenArea 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5
FAR 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
GSF =(B2Ͳ(Q2*B2))*R2 =(B3Ͳ(Q3*B3))*R3 =(B4Ͳ(Q4*B4))*R4 =(B5Ͳ(Q5*B5))*R5 =(B6Ͳ(Q6*B6))*R6 =(B7Ͳ(Q7*B7))*R7 =(B8Ͳ(Q8*B8))*R8 =(B9Ͳ(Q9*B9))*R9 =(B10Ͳ(Q10*B10))*R10 =(B11Ͳ(Q11*B11))*R11 =(B12Ͳ(Q12*B12))*R12 =(B13Ͳ(Q13*B13))*R13 =(B14Ͳ(Q14*B14))*R14 =(B15Ͳ(Q15*B15))*R15 =(B16Ͳ(Q16*B16))*R16 =(B17Ͳ(Q17*B17))*R17 =(B18Ͳ(Q18*B18))*R18 =(B19Ͳ(Q19*B19))*R19 =(B20Ͳ(Q20*B20))*R20 =(B21Ͳ(Q21*B21))*R21 =(B22Ͳ(Q22*B22))*R22 =(B23Ͳ(Q23*B23))*R23 =(B24Ͳ(Q24*B24))*R24 =(B25Ͳ(Q25*B25))*R25 =(B26Ͳ(Q26*B26))*R26 =(B27Ͳ(Q27*B27))*R27 =(B28Ͳ(Q28*B28))*R28
COMMON_SF =S2*0.0625 =S3*0.0625 =S4*0.0625 =S5*0.0625 =S6*0.0625 =S7*0.0625 =S8*0.0625 =S9*0.0625 =S10*0.0625 =S11*0.0625 =S12*0.0625 =S13*0.0625 =S14*0.0625 =S15*0.0625 =S16*0.0625 =S17*0.0625 =S18*0.0625 =S19*0.0625 =S20*0.0625 =S21*0.0625 =S22*0.0625 =S23*0.0625 =S24*0.0625 =S25*0.0625 =S26*0.0625 =S27*0.0625 =S28*0.0625
RES_SF =S2ͲT2 =S3ͲT3 =S4ͲT4 =S5ͲT5 =S6ͲT6 =S7ͲT7 =S8ͲT8 =S9ͲT9 =S10ͲT10 =S11ͲT11 =S12ͲT12 =S13ͲT13 =S14ͲT14 =S15ͲT15 =S16ͲT16 =S17ͲT17 =S18ͲT18 =S19ͲT19 =S20ͲT20 =S21ͲT21 =S22ͲT22 =S23ͲT23 =S24ͲT24 =S25ͲT25 =S26ͲT26 =S27ͲT27 =S28ͲT28
RES_CALC =U2/1000 =U3/1000 =U4/1000 =U5/1000 =U6/1000 =U7/1000 =U8/1000 =U9/1000 =U10/1000 =U11/1000 =U12/1000 =U13/1000 =U14/1000 =U15/1000 =U16/1000 =U17/1000 =U18/1000 =U19/1000 =U20/1000 =U21/1000 =U22/1000 =U23/1000 =U24/1000 =U25/1000 =U26/1000 =U27/1000 =U28/1000
RES_UNITS 6.402044324812 22.20348864825 4.678920506531 3.0025852605 2.934566139093 3.537598059843 5.011292390343 4.472988827343 3.46319963325 4.561829356218 3.187076793468 2.887567442156 4.438016357906 4.427874932639 4.339047854531 3.963157502062 3.180955672125 3.616386188625 3.537155738812 3.311823079687 3.75463051425 3.250395435656 3.215684372906 10.65946431965 4.315223392593 5.732900269031 8.494033377093
SOFT_COST 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35
CONST_COST 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150
PARKING_COST 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500 22500
EXP_RATIO 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
CAP_RATE 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075
TOTAL_SF =(W2*1000)+T2 =(W3*1000)+T3 =(W4*1000)+T4 =(W5*1000)+T5 =(W6*1000)+T6 =(W7*1000)+T7 =(W8*1000)+T8 =(W9*1000)+T9 =(W10*1000)+T10 =(W11*1000)+T11 =(W12*1000)+T12 =(W13*1000)+T13 =(W14*1000)+T14 =(W15*1000)+T15 =(W16*1000)+T16 =(W17*1000)+T17 =(W18*1000)+T18 =(W19*1000)+T19 =(W20*1000)+T20 =(W21*1000)+T21 =(W22*1000)+T22 =(W23*1000)+T23 =(W24*1000)+T24 =(W25*1000)+T25 =(W26*1000)+T26 =(W27*1000)+T27 =(W28*1000)+T28
CONST_COST =AC2*Y2 =AC3*Y3 =AC4*Y4 =AC5*Y5 =AC6*Y6 =AC7*Y7 =AC8*Y8 =AC9*Y9 =AC10*Y10 =AC11*Y11 =AC12*Y12 =AC13*Y13 =AC14*Y14 =AC15*Y15 =AC16*Y16 =AC17*Y17 =AC18*Y18 =AC19*Y19 =AC20*Y20 =AC21*Y21 =AC22*Y22 =AC23*Y23 =AC24*Y24 =AC25*Y25 =AC26*Y26 =AC27*Y27 =AC28*Y28
SOFT_COST =AD2*X2 =AD3*X3 =AD4*X4 =AD5*X5 =AD6*X6 =AD7*X7 =AD8*X8 =AD9*X9 =AD10*X10 =AD11*X11 =AD12*X12 =AD13*X13 =AD14*X14 =AD15*X15 =AD16*X16 =AD17*X17 =AD18*X18 =AD19*X19 =AD20*X20 =AD21*X21 =AD22*X22 =AD23*X23 =AD24*X24 =AD25*X25 =AD26*X26 =AD27*X27 =AD28*X28
PARKING_COST =O2*Z2 =O3*Z3 =O4*Z4 =O5*Z5 =O6*Z6 =O7*Z7 =O8*Z8 =O9*Z9 =O10*Z10 =O11*Z11 =O12*Z12 =O13*Z13 =O14*Z14 =O15*Z15 =O16*Z16 =O17*Z17 =O18*Z18 =O19*Z19 =O20*Z20 =O21*Z21 =O22*Z22 =O23*Z23 =O24*Z24 =O25*Z25 =O26*Z26 =O27*Z27 =O28*Z28
DEV_COST =AD2+AE2+AF2 =AD3+AE3+AF3 =AD4+AE4+AF4 =AD5+AE5+AF5 =AD6+AE6+AF6 =AD7+AE7+AF7 =AD8+AE8+AF8 =AD9+AE9+AF9 =AD10+AE10+AF10 =AD11+AE11+AF11 =AD12+AE12+AF12 =AD13+AE13+AF13 =AD14+AE14+AF14 =AD15+AE15+AF15 =AD16+AE16+AF16 =AD17+AE17+AF17 =AD18+AE18+AF18 =AD19+AE19+AF19 =AD20+AE20+AF20 =AD21+AE21+AF21 =AD22+AE22+AF22 =AD23+AE23+AF23 =AD24+AE24+AF24 =AD25+AE25+AF25 =AD26+AE26+AF26 =AD27+AE27+AF27 =AD28+AE28+AF28
GROSS_RENT =(W2*L2)*12 =(W3*L3)*12 =(W4*L4)*12 =(W5*L5)*12 =(W6*L6)*12 =(W7*L7)*12 =(W8*L8)*12 =(W9*L9)*12 =(W10*L10)*12 =(W11*L11)*12 =(W12*L12)*12 =(W13*L13)*12 =(W14*L14)*12 =(W15*L15)*12 =(W16*L16)*12 =(W17*L17)*12 =(W18*L18)*12 =(W19*L19)*12 =(W20*L20)*12 =(W21*L21)*12 =(W22*L22)*12 =(W23*L23)*12 =(W24*L24)*12 =(W25*L25)*12 =(W26*L26)*12 =(W27*L27)*12 =(W28*L28)*12
VACANCIES =((W2*J2)*(K2*Ͳ1))*12 =((W3*J3)*(K3*Ͳ1))*12 =((W4*J4)*(K4*Ͳ1))*12 =((W5*J5)*(K5*Ͳ1))*12 =((W6*J6)*(K6*Ͳ1))*12 =((W7*J7)*(K7*Ͳ1))*12 =((W8*J8)*(K8*Ͳ1))*12 =((W9*J9)*(K9*Ͳ1))*12 =((W10*J10)*(K10*Ͳ1))*12 =((W11*J11)*(K11*Ͳ1))*12 =((W12*J12)*(K12*Ͳ1))*12 =((W13*J13)*(K13*Ͳ1))*12 =((W14*J14)*(K14*Ͳ1))*12 =((W15*J15)*(K15*Ͳ1))*12 =((W16*J16)*(K16*Ͳ1))*12 =((W17*J17)*(K17*Ͳ1))*12 =((W18*J18)*(K18*Ͳ1))*12 =((W19*J19)*(K19*Ͳ1))*12 =((W20*J20)*(K20*Ͳ1))*12 =((W21*J21)*(K21*Ͳ1))*12 =((W22*J22)*(K22*Ͳ1))*12 =((W23*J23)*(K23*Ͳ1))*12 =((W24*J24)*(K24*Ͳ1))*12 =((W25*J25)*(K25*Ͳ1))*12 =((W26*J26)*(K26*Ͳ1))*12 =((W27*J27)*(K27*Ͳ1))*12 =((W28*J28)*(K28*Ͳ1))*12
EXP_EXPENSES =(AH2*AA2)*Ͳ1 =(AH3*AA3)*Ͳ1 =(AH4*AA4)*Ͳ1 =(AH5*AA5)*Ͳ1 =(AH6*AA6)*Ͳ1 =(AH7*AA7)*Ͳ1 =(AH8*AA8)*Ͳ1 =(AH9*AA9)*Ͳ1 =(AH10*AA10)*Ͳ1 =(AH11*AA11)*Ͳ1 =(AH12*AA12)*Ͳ1 =(AH13*AA13)*Ͳ1 =(AH14*AA14)*Ͳ1 =(AH15*AA15)*Ͳ1 =(AH16*AA16)*Ͳ1 =(AH17*AA17)*Ͳ1 =(AH18*AA18)*Ͳ1 =(AH19*AA19)*Ͳ1 =(AH20*AA20)*Ͳ1 =(AH21*AA21)*Ͳ1 =(AH22*AA22)*Ͳ1 =(AH23*AA23)*Ͳ1 =(AH24*AA24)*Ͳ1 =(AH25*AA25)*Ͳ1 =(AH26*AA26)*Ͳ1 =(AH27*AA27)*Ͳ1 =(AH28*AA28)*Ͳ1
NOI =AH2+AI2+AJ2 =AH3+AI3+AJ3 =AH4+AI4+AJ4 =AH5+AI5+AJ5 =AH6+AI6+AJ6 =AH7+AI7+AJ7 =AH8+AI8+AJ8 =AH9+AI9+AJ9 =AH10+AI10+AJ10 =AH11+AI11+AJ11 =AH12+AI12+AJ12 =AH13+AI13+AJ13 =AH14+AI14+AJ14 =AH15+AI15+AJ15 =AH16+AI16+AJ16 =AH17+AI17+AJ17 =AH18+AI18+AJ18 =AH19+AI19+AJ19 =AH20+AI20+AJ20 =AH21+AI21+AJ21 =AH22+AI22+AJ22 =AH23+AI23+AJ23 =AH24+AI24+AJ24 =AH25+AI25+AJ25 =AH26+AI26+AJ26 =AH27+AI27+AJ27 =AH28+AI28+AJ28
EST_VAL =AK2/AB2 =AK3/AB3 =AK4/AB4 =AK5/AB5 =AK6/AB6 =AK7/AB7 =AK8/AB8 =AK9/AB9 =AK10/AB10 =AK11/AB11 =AK12/AB12 =AK13/AB13 =AK14/AB14 =AK15/AB15 =AK16/AB16 =AK17/AB17 =AK18/AB18 =AK19/AB19 =AK20/AB20 =AK21/AB21 =AK22/AB22 =AK23/AB23 =AK24/AB24 =AK25/AB25 =AK26/AB26 =AK27/AB27 =AK28/AB28
RESID_VAL =AL2ͲAG2 =AL3ͲAG3 =AL4ͲAG4 =AL5ͲAG5 =AL6ͲAG6 =AL7ͲAG7 =AL8ͲAG8 =AL9ͲAG9 =AL10ͲAG10 =AL11ͲAG11 =AL12ͲAG12 =AL13ͲAG13 =AL14ͲAG14 =AL15ͲAG15 =AL16ͲAG16 =AL17ͲAG17 =AL18ͲAG18 =AL19ͲAG19 =AL20ͲAG20 =AL21ͲAG21 =AL22ͲAG22 =AL23ͲAG23 =AL24ͲAG24 =AL25ͲAG25 =AL26ͲAG26 =AL27ͲAG27 =AL28ͲAG28
VALUE_SF =AM2/B2 =AM3/B3 =AM4/B4 =AM5/B5 =AM6/B6 =AM7/B7 =AM8/B8 =AM9/B9 =AM10/B10 =AM11/B11 =AM12/B12 =AM13/B13 =AM14/B14 =AM15/B15 =AM16/B16 =AM17/B17 =AM18/B18 =AM19/B19 =AM20/B20 =AM21/B21 =AM22/B22 =AM23/B23 =AM24/B24 =AM25/B25 =AM26/B26 =AM27/B27 =AM28/B28
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