An Evaluation of Property Tax Regressivity in Allegheny County, Pennsylvania

May 2024 · 24 minute read

The property tax is the single largest source of revenue for American local governments. Cities, counties, school districts, and special districts raise roughly $500 billion per year in property taxes, accounting for 72% of local taxes and 47% of locally raised revenue (U.S. Census Bureau 2016). Whether residents rent or own, property taxes directly or indirectly impact almost everyone.

In many cities, however, property taxes are inequitable; low-value properties face higher tax assessments, relative to their actual sale price, than do high-value properties, resulting in regressive taxation that burdens low-income residents disproportionately.

The standard approach for evaluating the quality and fairness of assessments is through a sales ratio study (International Association of Assessing Officers 2013). A property’s sales ratio is defined as the assessed value divided by the sale price. A sales ratio study evaluates the extent of regressivity in a jurisdiction, along with other aspects of assessment performance, by studying sales ratios for properties that sold within a specific time period. A system in which less expensive homes are systematically assessed at higher sales ratios than more expensive homes is regressive.

This report presents a basic sales ratio study for Allegheny County, Pennsylvania, based on data from CoreLogic. CoreLogic collects property data from assessors (and other sources) across the country. We use data for residential properties that sold between 2012 and 2020 (the most recent year available for this jurisdiction) and are classified as arm’s-length transactions by CoreLogic. For each home that sold, we compute the sales ratio as the assessed value in place on January 1 of the sale year divided by the sale price. For more details, see the Appendix.

The relationship between assessments and sale prices is regressive if less valuable homes are assessed at higher rates (relative to the value of the home) than more valuable homes. To evaluate regressivity in assessments, Figure 2.1 presents a binned scatter plot of sales ratios against sale prices.

For this graph, property sales have been sorted into deciles (10 bins of equal size based on sale price), each representing 10% of all properties sold. Each dot represents the average sale price and average sales ratio for each respective decile of properties. This graph compares the most recent values for 2020 (solid line) with the average across all years of observation from 2012 to 2020 (dashed line). All values were adjusted for inflation to 2020 dollars to facilitate comparisons.

If sale prices are a fair indication of market value and if assessments were fair and accurate, Figure 2.1 would be a flat line indicating that sales ratios do not vary systematically according to sale price. A downward sloping line indicates that less expensive homes are over-assessed compared to more expensive homes and is evidence of regressivity.

In 2020, the most expensive homes (the top decile) were assessed at 64.3% of their value and the least expensive homes (the bottom decile) were assessed at 67.0%. In other words, the least expensive homes were assessed at 1.04 times the rate applied to the most expensive homes. Across our sample from 2012 to 2020, the most expensive homes were assessed at 69.2% of their value and the least expensive homes were assessed at 86.8%, which is 1.25 times the rate applied to the most expensive homes.

Figure 2.1

Figure 2.2 shows the share of properties in each decile that were overassessed or underassessed. relative to the median rate of assessment. That is, a property is classified as overassessed if its sales ratio is above the median sales ratio for the jurisdiction, and classified as underassessed if its sales ratio is below the median. If errors were made randomly, each decile would have 50% of properties overassessed and 50% underassessed. When lower value homes are more likely to be overassessed than higher value homes, it is evidence of regressivity. In Allegheny County, Pennsylvania, 72% of the lowest value homes are overassessed and 53% of the highest value homes are overassessed.

Figure 2.2

Assessed values are the basis on which taxes are calculated, meaning that inequities in assessments will be transmitted into inequities in tax rates. In this section, we evaluate effective tax rates – a property’s tax bill divided by its sale price – according to sale price.

Importantly, the effective tax rate is the actual tax rate paid inclusive of exemptions or other tax breaks. Often, because exemptions are more likely to target low-valued properties, they may offset some of the increased taxation resulting from over-assessment. In other words, tax rates will often be somewhat less regressive than assessments. Tax rates also will vary widely based on municipal and school district boundaries. This section analyzes tax rates across the entire county. A brief analysis by school district, which roughly approximates a single taxing district, is also presented in the Appendix.

Consistent with Figure 2.1, in 2020, the most expensive homes (the top decile) had an effective tax rate of 1.77%, while the rate for the least expensive homes (bottom decile) was 2.33%, which is 1.32 times the rate applied to the most expensive homes. Across our sample from 2012 to 2020, the most expensive homes had an effective tax rate of 1.90% of their value and the least expensive homes had an effective tax rate of 3.00%, which is 1.58 times the rate applied to the most expensive homes.

Figure 3.1

Table 3.1

Table 3.1 presents a simple analysis of effective tax rate by sale decile (where sale decile 1 consists of the most inexpensive homes in this jurisdiction and 10 the most expensive). A property’s “fair” tax bill is the bill that would have been charged if the property was taxed at the average rate, and the “shift” is the difference between the fair bill and the actual bill. In 2020, the average effective tax rate in Allegheny County, Pennsylvania was 1.78%.

Tax Rate by Sale Decile
Tax YearSale DecileEffective Tax RateAverage SaleAverage Tax BillFair Tax BillAverage Shift
202012.33%$58,633$1,324.74$1,042.28$282.46
202021.96%$107,591$2,109.05$1,912.56$196.49
202031.72%$137,824$2,367.63$2,449.97-$82.34
202041.69%$161,214$2,726.91$2,865.76-$138.85
202051.64%$184,918$3,035.63$3,287.13-$251.50
202061.64%$214,109$3,510.55$3,806.03-$295.47
202071.62%$249,279$4,032.74$4,431.21-$398.47
202081.66%$298,861$4,964.83$5,312.59-$347.77
202091.75%$377,226$6,612.73$6,705.62-$92.90
2020101.77%$643,050$11,339.75$11,430.94-$91.19

For example, in 2020, the average property in the bottom decile sold for a price of $58,633 and had a tax bill of $1,324.74. If this property was taxed at the average rate of all other properties, its fair bill would be $1,042.28, meaning that the homeowner overpaid by $282.46, or 27.1% above the fair tax. Correspondingly, the average property in the top decile sold for $643,050 and had a tax bill of $11,339.75. If this property was taxed at the average rate of all other property, its fair bill would be $11,430.94, meaning that the homeowner underpaid by $91.19, or 0.8% below the fair tax.

Sections 2 and 3 provide graphical evidence of regressivity in property assessments and taxes, but they do not provide a statistical evaluation. In this section, we report several standard statistics used in the evaluation of assessment quality.

The International Association of Assessing Officers (IAAO) defines standards for assessments including standards for uniformity and regressivity (International Association of Assessing Officers 2013). A detailed overview and definition of each measure can be found in the Appendix.

Coefficient of Dispersion (COD)

The COD is a measure of assessment uniformity, or horizontal equity. It is the average absolute percentage difference from the median sales ratio. For instance, a COD of 10 means that properties have ratios that on average deviate by 10 percent from the median ratio. The IAAO specifies that the acceptable range for COD is below 15, which is shaded in Figure 4.1. For 2020, the COD in Allegheny County, Pennsylvania was 24.52. Year-to-year changes in COD can also reveal changes assessment accuracy. In this case, assessments do not meet the IAAO standard for uniformity and are relatively unchanged over the last two years.

Figure 4.1

Price-Related Differential (PRD)

The PRD is a measure of regressivity, or vertical equity. A PRD of 1 indicates that homes are assessed at the same rate regardless of their sale price. A PRD greater than 1 indicates that less expensive homes are assessed at higher rates than more expensive homes, while a PRD less than 1 represents the opposite situation. The IAAO specifies that the acceptable range of PRD is .98 to 1.03, which is depicted as the shaded region of Figure 4.2. In 2020, the PRD in Allegheny County, Pennsylvania, was 0.984 which meets the IAAO standard for vertical equity. PRD values are relatively unchanged over the last two years.

Figure 4.2

Coefficient of Price-Related Bias (PRB)

The PRB is another quantitative measure of regressivity (vertical equity) which is an alternative to the PRD. PRB is a measure of how much assessed values change as a property’s market value increases. The IAAO specifies that the acceptable range for PRB is between -0.05 and 0.05, which is depicted as the shaded region in the Figure 4.3. In 2020, the PRB in Allegheny County, Pennsylvania was 0.066 which indicates that sales ratios increase by 6.6% when home values double. This does not meet the IAAO standard. PRB values have been relatively relatively unchanged over the last two years.

Figure 4.3

By placing homes geographically within individual census tracts (“geocoding”), we are able to explore how assessments differ across geography. We are also able to correlate assessment rates with census demographics on the tract level.

Geographic Variation

In most jurisdictions, properties of different values are not randomly distributed but rather spatially clustered. If so, then regressivity in assessments will result in some neighborhoods of the jurisdiction being over-assessed and others under-assessed. The two maps below show the spatial distribution of sales ratios (Figure 5.1) and effective tax rates (Figure 5.2), respectively.

Figure 5.1

Figure 5.2

Note that tax rates may vary across jurisdictions for reasons unrelated to assessment quality.

Demographic Variation

When there are correlations between property values and demographics, assessment regressivity will result in differential taxation by demographics. This section presents a basic demographic profile of Allegheny County, Pennsylvania, based on the 2018 American Community Survey produced by the U.S. Census Bureau (Walker 2019a). Next is an analysis of the correlations between census demographics at the tract level and sales ratios and tax rates. Essentially, these correlations reveal whether properties in different sorts of neighborhoods experience different levels of assessment and taxation. It is important to emphasize that we do not have data on the demographics of individual property owners and so these tract-level demographic correlations do not necessarily imply that individual owners with different demographics are assessed or taxed differentially.

Table 5.2.1: A Demographic Profile of Allegheny County, Pennsylvania

Total Population1,229,605
Percent Non-White21%
Percent in Poverty13%
Percent Homeowners65%
Percent with Bachelor (or higher)40%
Per Capita Income$35,280
Median Age40.9
Median Home Value$140,600
Median Home Value (State Rank)37th

Demographic Correlates: Sales Ratios

Table 5.2.2 presents results from an analysis in which sales ratios are regressed against census tract demographics. Each row of the table represents the coefficient from a different bivariate regression of sales ratios against the census variable in question.

Table 5.2.2

An example interpretation of this table; a 1% increase in the percentage of individuals with a high school education is correlated with a 0.08% increase in sales ratio.

Census Tract Characteristics Regressed on Mean sales ratio
VariableCoefficientP ValueSignificance
Non-Hispanic White Population (Percentage Points)0.02%0.21Not Significant
Population in Poverty (Percentage Points)-0.14%0.00Significant
Share of Homes Vacant (Percentage Points)-0.08%0.10Not Significant
Share of Homeowners (Percentage Points)0.13%0.00Significant
Share of Single Unit Homes (Percentage Points)0.11%0.00Significant
Share in Same Home as Last Year (Percentage Points)0.29%0.00Significant
High School Education or Higher (Percentage Points)0.08%0.38Not Significant
College Education or Higher (Percentage Points)-0.06%0.00Significant
Per Capita Income ($1000s)0.03%0.26Not Significant
Median Household Income ($1000s)0.04%0.00Significant
Median Age (Years)0.44%0.00Significant
Median Home Value ($1000s)0.00%0.43Not Significant

Figure 5.2.1 presents binned scatterplots of the average assessment rate by census tract for selected demographic variables.

Figure 5.2.1

Demographic Correlates: Effective Tax Rates

Table 5.2.3 shows relationships between effective tax rates and census demographics at the tract level. Each row of the table represents the coefficient from a different bivariate regression of the effective tax rate against the census variable in question.

Table 5.2.3

Census Tract Characteristics Regressed on Mean Tax Rate
VariableCoefficientP ValueSignificance
Non-Hispanic White Population (Percentage Points)-0.16%0.24Not Significant
Population in Poverty (Percentage Points)-0.13%0.59Not Significant
Share of Homes Vacant (Percentage Points)0.68%0.10Not Significant
Share of Homeowners (Percentage Points)0.22%0.10Not Significant
Share of Single Unit Homes (Percentage Points)0.39%0.01Significant
Share in Same Home as Last Year (Percentage Points)1.28%0.00Significant
High School Education or Higher (Percentage Points)-0.92%0.19Not Significant
College Education or Higher (Percentage Points)-0.96%0.00Significant
Per Capita Income ($1000s)-0.74%0.00Significant
Median Household Income ($1000s)-0.27%0.01Significant
Median Age (Years)2.39%0.00Significant
Median Home Value ($1000s)-0.17%0.00Significant

An example interpretation of this table; a 1% increase in the percentage of individuals with a high school education is correlated with a -0.92% decrease in effective tax rate.

Figure 5.2.2 presents the average tax rate by census tract for selected demographic variables.

Figure 5.2.2

Figure 6.1, Figure 6.2, and Table 6.1 compare this jurisdiction to the rest of the nation. Higher values (to the right side) are more regressive.

Figure 6.1

Figure 6.2

Table 6.1

National and State Ranks
90 to 10 ratio0.8
90 to 10 ratio National Rank1799/2775
90 to 10 ratio State Rank54/66
80 to 20 ratio0.797
80 to 20 ratio National Rank1348/2775
80 to 20 ratio State Rank46/66
PRD1.042
PRD National Rank1986/2775
PRD State Rank48/66
Number of Unranked Counties Nationwide353
Number of Unranked Counties Statewide1

Allegheny County, Pennsylvania is ranked 12th least regressive out of 66 Pennsylvania counties in our sample. Note: 1 counties were excluded from our analysis due to lack of data. Home values in Allegheny County, Pennsylvania are above average nationwide and regressivity levels are below average.

Here detailed information on our analysis is presented alongside reference information.

IAAO Standards

The International Association of Assessing Officers (IAAO) defines standards for assessments including standards for uniformity and vertical equity (International Association of Assessing Officers 2013). Uniform assessments assess similar properties with as little variability as possible. Vertically equitable assessments assess properties at similar rates regardless of a property’s value. The three main standards are:

Table 7.1.1

IAAO Standards for Single Family Residential Properties
ParameterStandard MinimumStandard Maximum
COD5.0015.00
PRD0.981.03
PRB-0.050.05

IAAO Statistics by Year

The following is a detailed breakdown by year of our estimates of IAAO standards and their bootstrapped confidence intervals. These estimates form the basis of our COD, PRD, and PRB plots.

Table 7.2.1

Calculated Values for COD, PRD, and PRB
Tax YearArms Length SalesAverage Sale PriceCODPRDPRB
201212751$130,00027.0898 ± 0.4091.0766 ± 0.008-0.0448 ± 0.006
201314470$134,40023.9001 ± 0.3591.0566 ± 0.005-0.0351 ± 0.005
201414268$138,00023.7128 ± 0.3311.0607 ± 0.005-0.0414 ± 0.005
201514657$145,00022.7639 ± 0.3661.0459 ± 0.007-0.0223 ± 0.005
201613388$154,00022.6495 ± 0.2911.0284 ± 0.004-0.0077 ± 0.005
201713788$160,05022.7567 ± 0.3191.0174 ± 0.0040.0057 ± 0.005
201814721$168,40023.9662 ± 0.3331.0117 ± 0.0040.0242 ± 0.005
201915657$175,00025.5063 ± 0.3711.019 ± 0.0050.0215 ± 0.005
202014597$199,00024.519 ± 0.3770.9843 ± 0.0040.0659 ± 0.005

Sales Ratio by Decile by Year

The following Figure 7.3.1 replicates Figure 2.1 from Sales Ratio Analysis. For each panel of the Figure 7.3.1, the current year is highlighted in blue and other years are in gray.

Figure 7.3.1

Table 7.3.1 shows the data underling the Figure 2.1 from Sales Ratio Analysis.

Table 7.3.1

Sales Ratio by Sale Decile and Year
Sale YearSale DecileAverage Sale PriceMean RatioMedian Ratio
20201$58,6330.67030.6810
20202$107,5910.58530.5721
20203$137,8240.53440.5300
20204$161,2140.54500.5435
20205$184,9180.53960.5442
20206$214,1090.54790.5465
20207$249,2790.54980.5525
20208$298,8610.57180.5723
20209$377,2260.60900.6203
202010$643,0500.64330.6573
Sales Ratio by Sale Decile (all years)
Sale DecileAverage Sale PriceMean RatioMedian Ratio
1$43,9490.86770.8831
2$82,6180.78190.7600
3$111,0650.72510.6984
4$136,2710.68220.6648
5$159,9890.66660.6615
6$185,6940.66130.6610
7$218,4170.65680.6577
8$266,5660.65920.6651
9$345,4780.67150.6836
10$620,3630.69160.7032

Measurement Error and Spurious Regressivity

One limitation of sales ratio studies is that a property’s sale price may be an imperfect indication of its true market value. Given inevitable random factors in the sale of any individual property, the final price may include some “noise.” If properties are spatially cluttered, this will introduce measurement error into the analysis, which could lead to the appearance of regressivity when there is none. For instance, consider two hypothetical homes that are identical and each worth $100,000. If both homes went up for sale at the same time, one home might fetch a price of $105,000, say if the seller were a particularly savvy negotiator, while the other home might garner only $95,000, say if the buyer were a particularly savvy negotiator. If the assessor appropriately valued both homes at $100,000, a sales ratio analysis would indicate regressivity (the higher-priced home is under-assessed and the lower-priced home would be over-assessed, relative to the sale price). While there is no reliable correction for measurement error of this kind, as long as the extent of measurement error is small, relative to the price, the extent of bias will also be small.

We use Monte Carlo simulations to estimate the extent of measurement error that would need to exist for any of our tests to falsely show regressivity due to measurement error. We compare our results with thousands of simulated scenarios to determine the likelihood that our results would be reproduced in the absence of regressivity.

The simulations are conducted as follows. First, using the same data set that was used for the main analysis, we construct a simulated sale price for each property that is set equal to the actual assessed value. In this scenario where simulated sale prices always equal assessed value, the assessments will appear to be perfect according to all of our metrics and there will be no regressivity. We then “jitter” the simulated sale prices by adding random noise drawn from a normal distribution with a mean of zero and a standard deviation of k percent. While we think that measurement error on the order of only a few percentage points is plausible in real data, we consider values of k ranging from 1 to 25. To be concrete, when k is equal to one percent, the simulated sale price is set equal to the assessed value multiplied by (1 plus a random shock drawn from a normal distribution with a mean of zero and a standard deviation of .01). The shock is drawn independently for each property in the data set. For each value of k, we run 100 simulations and record the value of each metric computed in each simulation. The mean value of each metric across the 100 simulations is reported for each value of k.

Intuitively, this exercise shows how much spurious regressivity would exist if assessed values were accurate on average but sale prices contained random noise of a given value, k. We then compare the actual value of the regressivity metrics from the real data with the values from the simulated data to recover an estimate of the amount of noise that would be necessary to produce the observed regressivity statistic if there were in fact no bias in assessments.

Figure 7.4.1 shows the results of our simulations. The dots in each graph show the mean value of the metric in question across the 100 simulations for each value of k. The solid line in each graph shows the value of the metric in the real data. We show simulations for COD, PRD, PRB, and each coefficient in Table 7.4.1.

Figure 7.4.1

Effective Tax Rate by School District

In many jurisdictions, the tax rate varies based on the overlap of taxing districts, such as municipality, school district, or special district. The final determination of tax bills varies widely place-to-place, but the school district often accounts for the largest component of property taxes. This section presents estimates for the largest school district in this county (contained within one city) and summary information on all unified (K-12) school districts in which sufficient property information is available.

Pittsburgh School District

Figure 7.5.1

In 2020, the most expensive homes (the top decile) had an effective tax rate of 1.20%, while the rate for the least expensive homes (bottom decile) was 1.51%, which is 1.26 times the rate applied to the most expensive homes. Across our sample from 2012 to 2020, the most expensive homes had an effective tax rate of 1.41% of their value and the least expensive homes had an effective tax rate of 2.36%, which is 1.67 times the rate applied to the most expensive homes.

All Districts

Table 7.5.1

Tax Rate by School District
DistrictCityNumber of SalesEffective Tax RateAverage SaleAverage Tax BillBottom Decile Tax RateTop Decile Tax RateBottom Decile Sale PriceTop Decile Sale Price
Avonworth School DistrictPITTSBURGH9592.03%$301,619$5,903.792.55%1.80%$91,908$626,410
Baldwin-Whitehall School DistrictPITTSBURGH42962.25%$166,445$3,574.723.32%1.91%$79,021$293,236
Bethel Park School DistrictBETHEL PARK35162.24%$211,405$4,604.542.97%2.06%$102,652$369,392
Brentwood Borough School DistrictPITTSBURGH12073.15%$121,893$3,561.524.82%2.37%$53,901$209,298
Carlynton School DistrictCARNEGIE10172.44%$160,043$3,716.803.45%2.22%$49,019$346,634
Carlynton School DistrictPITTSBURGH5572.41%$172,254$3,886.393.39%1.93%$67,994$317,906
Chartiers Valley School DistrictBRIDGEVILLE8072.08%$163,260$3,201.882.90%1.75%$64,013$341,989
Chartiers Valley School DistrictCARNEGIE9282.02%$177,623$3,372.702.78%1.81%$62,105$375,867
Chartiers Valley School DistrictPITTSBURGH15402.02%$180,603$3,529.392.57%1.73%$86,626$308,438
Clairton City School DistrictCLAIRTON5042.42%$46,538$843.364.02%1.06%$10,325$134,644
Cornell School DistrictCORAOPOLIS6343.03%$105,269$2,872.674.79%2.31%$31,631$203,567
Deer Lakes School DistrictGIBSONIA5402.24%$232,436$4,986.593.04%1.81%$96,643$427,711
East Allegheny School DistrictNORTH VERSAILLES8483.17%$94,365$2,784.234.75%2.48%$35,492$183,171
Elizabeth Forward School DistrictELIZABETH8672.33%$145,300$3,219.503.22%2.11%$52,479$293,957
Fox Chapel Area School DistrictPITTSBURGH25912.09%$431,280$8,807.272.61%2.06%$75,168$1,200,701
Gateway School DistrictMONROEVILLE28632.20%$173,410$3,701.823.09%2.06%$76,995$353,256
Hampton Township School DistrictALLISON PARK12571.91%$317,431$6,011.492.30%1.93%$116,569$695,726
Hampton Township School DistrictGIBSONIA7611.91%$315,055$6,237.712.31%2.26%$121,269$903,319
Highlands School DistrictNATRONA HEIGHTS11262.60%$126,064$3,107.043.42%2.38%$31,331$273,353
Keystone Oaks School DistrictPITTSBURGH25142.34%$172,068$3,667.623.64%1.65%$76,472$324,733
McKeesport Area School DistrictMCKEESPORT11532.32%$59,260$1,213.053.37%1.84%$11,588$163,814
McKeesport Area School DistrictWHITE OAK7582.16%$121,517$2,479.112.89%1.81%$44,422$229,372
Montour School DistrictCORAOPOLIS8311.84%$229,047$4,148.872.21%1.83%$95,126$447,325
Montour School DistrictMC KEES ROCKS11451.86%$227,224$4,140.482.51%1.84%$91,180$431,236
Montour School DistrictPITTSBURGH10591.96%$178,055$3,342.192.71%1.72%$78,826$422,565
Moon Area School DistrictCORAOPOLIS32752.02%$262,846$5,216.652.38%2.10%$64,224$597,879
Mount Lebanon School DistrictPITTSBURGH43632.40%$325,820$7,525.823.11%2.09%$141,774$681,703
North Allegheny School DistrictALLISON PARK6011.82%$293,881$5,220.122.26%1.71%$152,845$537,942
North Allegheny School DistrictPITTSBURGH24511.78%$285,678$4,995.542.15%1.68%$115,376$558,528
North Allegheny School DistrictSEWICKLEY6291.89%$444,000$8,320.842.20%1.90%$160,603$900,727
North Allegheny School DistrictWEXFORD20391.93%$377,361$7,219.702.20%1.97%$158,553$779,407
North Hills School DistrictPITTSBURGH46071.95%$195,701$3,631.642.66%1.63%$86,334$367,272
Northgate School DistrictPITTSBURGH11922.72%$136,833$3,396.614.13%1.81%$53,807$240,177
Penn Hills School DistrictPITTSBURGH32322.84%$105,056$2,743.314.14%2.15%$41,889$198,989
Penn Hills School DistrictVERONA14242.68%$107,860$2,678.283.99%2.05%$43,078$179,788
Pine-Richland School DistrictGIBSONIA19982.01%$394,429$7,921.892.45%2.04%$144,203$956,036
Pine-Richland School DistrictWEXFORD8991.97%$465,750$9,215.672.20%2.04%$188,758$881,616
Pittsburgh School DistrictPITTSBURGH271211.60%$215,131$3,032.602.61%1.37%$33,694$681,609
Plum Borough School DistrictPITTSBURGH19192.20%$181,014$3,898.963.01%2.23%$82,745$346,249
Quaker Valley School DistrictSEWICKLEY17712.14%$520,187$10,788.022.59%2.02%$108,422$1,536,315
Riverview School DistrictOAKMONT8152.58%$252,671$6,112.473.83%2.13%$85,215$558,082
Shaler Area School DistrictALLISON PARK5422.09%$215,268$4,367.513.01%1.86%$130,754$336,197
Shaler Area School DistrictGLENSHAW14812.11%$210,301$4,282.073.06%1.85%$112,669$341,917
Shaler Area School DistrictPITTSBURGH22982.27%$156,218$3,291.183.34%1.86%$47,514$301,217
South Allegheny School DistrictMCKEESPORT6182.37%$81,776$1,788.993.63%1.78%$30,682$150,628
South Fayette Township School DistrictBRIDGEVILLE13022.66%$238,143$6,345.942.82%2.69%$82,206$507,563
South Fayette Township School DistrictMC DONALD5442.67%$316,803$8,359.572.99%2.66%$137,261$538,745
South Park School DistrictSOUTH PARK10442.41%$189,654$4,427.853.22%2.33%$77,814$357,482
Steel Valley School DistrictHOMESTEAD13882.84%$96,588$2,424.174.25%1.87%$19,579$191,042
Sto-Rox School DistrictMC KEES ROCKS9603.07%$68,988$1,854.234.35%1.96%$16,950$155,666
Upper St. Clair School DistrictPITTSBURGH26222.49%$365,761$8,960.783.00%2.48%$156,933$789,257
West Allegheny School DistrictIMPERIAL8721.97%$197,338$3,829.142.21%1.76%$74,922$333,751
West Allegheny School DistrictOAKDALE7462.05%$222,826$4,486.402.65%1.99%$98,864$443,067
West Jefferson Hills School DistrictCLAIRTON9292.29%$239,865$5,376.422.91%2.22%$74,168$491,043
West Jefferson Hills School DistrictPITTSBURGH9972.26%$190,462$4,179.643.16%2.04%$102,159$318,684
West Mifflin Area School DistrictWEST MIFFLIN18832.85%$110,732$2,945.134.24%2.35%$44,252$206,475
Wilkinsburg Borough School DistrictPITTSBURGH9413.76%$125,592$3,965.385.41%2.49%$17,386$347,495
Woodland Hills School DistrictPITTSBURGH35682.78%$169,534$4,388.163.87%2.34%$45,800$390,717
Woodland Hills School DistrictTURTLE CREEK5012.98%$80,919$2,184.164.00%2.49%$18,539$222,762

Regression-Based Estimates of Regressivity

Aside from the standard PRD and PRB tests recommended by the IAAO, several alternative metrics have been proposed by academic researchers (Hodge et al. 2017). Table 7.6.1 presents estimates of the most commonly used models.

Model (1) shows a regression of assessed value (AV) against sale price. The coefficient on sale price should equal the jurisdiction’s legally mandated assessment rate (i.e., for each dollar of sale price, the assessed value should increase by the mandated assessment rate). In a jurisdiction where the assessment rate is 100%, the coefficient should be 1. A coefficient smaller than the assessment ratio indicates regressivity.

Model (2) shows a regression of the log of assessed value against the log of sale price, which estimates the elasticity of assessed values with respect to sale price. In the absence of regressivity, this coefficient should be 1. A value less than 1 indicates regressivity.

Model (3) shows a regression log sales ratios against log sale prices. In the absence of regressivity, this coefficient should be zero. A coefficient less than zero is an indication of regressivity.

Table 7.6.1

Dependent Variable
AVlog(AV)log(Sales_Ratio)
(1)(2)(3)
SALE_PRICE0.65***
(0.001)
log(SALE_PRICE)0.91***-0.09***
(0.001)(0.001)
Constant4,919.31***0.72***0.72***
(227.53)(0.02)(0.02)
Observations128,263128,263128,263
R20.810.780.04
Adjusted R20.810.780.04
Note:p<0.1; p<0.05; p<0.01
Cheng, Joe, Bhaskar Karambelkar, and Yihui Xie. 2018. Leaflet: Create Interactive Web Maps with the JavaScript ’Leaflet’ Library. https://CRAN.R-project.org/package=leaflet. Cook County Assessor’s Office. 2019. Open Code Base. https://gitlab.com/ccao-data-science---modeling. Hadley Wickham and Winston Chang, et. al. 2019. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2. Hlavac, Marek. 2018. Stargazer: Well-Formatted Regression and Summary Statistics Tables. https://CRAN.R-project.org/package=stargazer. Hodge, Timothy R., Daniel P. McMillen, Gary Sands, and Mark Skidmore. 2017. “Assessment Inequity in a Declining Housing Market: The Case of Detroit.” Real Estate Economics 45 (2): 237–58. https://doi.org/10.1111/1540-6229.12126. International Association of Assessing Officers. 2013. Standard on Ratio Studies. https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf. JJ Allaire and Yihui Xie, et. al. 2019. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown. Pebesma, Edzer. 2019. Sf: Simple Features for r. https://CRAN.R-project.org/package=sf. U.S. Census Bureau. 2016. 2016 Annual Surveys of State and Local Government Finances. https://www.census.gov/data/datasets/2016/econ/local/public-use-datasets.html. Walker, Kyle. 2019a. Tidycensus: Load US Census Boundary and Attribute Data as ’Tidyverse’ and ’Sf’-Ready Data Frames. https://CRAN.R-project.org/package=tidycensus. ———. 2019b. Tigris: Load Census TIGER/Line Shapefiles. https://CRAN.R-project.org/package=tigris.

Copyright 2022, The University of Chicago, Center for Municipal Finance

Please cite as: “Berry, Christopher. 2022. An Evaluation of Property Tax Regressivity in Allegheny County, Pennsylvania. Policy Brief. The University of Chicago, Center for Municipal Finance. www.propertytaxproject.uchicago.edu”

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