Chapter 2

Descriptive Analysis of Neighborhood Change

Neighborhood Change

Chapter 2 is an examination of the national trends in median home values from 1990 to 2000. Included in the analysis is a selection of variables to measure gentrification nationally and metro areas.

Metrics: raw data

The raw data originated from the Longitudinal Tabulated Database (LTD) that uses the long-form census from 1970 to 2010, and the meta-data. The data dictionary was generated from the LTD & merged with the census dataset. The purposes of this study, the years before 1990 were dropped. The spatial files were merged with the census data & the ACS data to create the dorling chloropeth maps.

## 
## rural urban 
## 12971 59722

Identify Common Variables

Next, the team identified the common variables to use in the analysis.

## [1] "SHARED VARIABLES:"
##   [1] "a15asn"  "a15blk"  "a15hsp"  "a15ntv"  "a15wht"  "a18und"  "a60asn" 
##   [8] "a60blk"  "a60hsp"  "a60ntv"  "a60up"   "a60wht"  "a75up"   "ag15up" 
##  [15] "ag25up"  "ag5up"   "ageasn"  "ageblk"  "agehsp"  "agentv"  "agewht" 
##  [22] "asian"   "china"   "clf"     "cni16u"  "col"     "cuban"   "dapov"  
##  [29] "dbpov"   "dflabf"  "dfmpov"  "dhpov"   "dis"     "dmulti"  "dnapov" 
##  [36] "dpov"    "dwpov"   "empclf"  "family"  "fb"      "fhh"     "filip"  
##  [43] "flabf"   "geanc"   "gefb"    "h10yrs"  "h30old"  "haw"     "hh"     
##  [50] "hha"     "hhb"     "hhh"     "hhw"     "hinc"    "hinca"   "hincb"  
##  [57] "hinch"   "hincw"   "hisp"    "hs"      "hu"      "incpc"   "india"  
##  [64] "iranc"   "irfb"    "itanc"   "itfb"    "japan"   "korea"   "lep"    
##  [71] "manuf"   "mar"     "mex"     "mhmval"  "mrent"   "multi"   "n10imm" 
##  [78] "n65pov"  "napov"   "nat"     "nbpov"   "nfmpov"  "nhblk"   "nhpov"  
##  [85] "nhwht"   "nnapov"  "npov"    "ntv"     "nwpov"   "ohu"     "olang"  
##  [92] "own"     "pop"     "pr"      "prof"    "rent"    "ruanc"   "rufb"   
##  [99] "scanc"   "scfb"    "semp"    "tractid" "unemp"   "vac"     "vet"    
## [106] "viet"    "wds"    
## [1] "NOT SHARED:"
## [1] "ag16cv"  "ag18cv"  "hu00sp"  "hu90sp"  "ohu00sp" "ohu90sp" "pop90.1"

##     type variables
## 1 shared    a15asn
## 2 shared    a15blk
## 3 shared    a15hsp
## 4 shared    a15ntv
## 5 shared    a15wht
## 6 shared    a18und

## Create Dataset for Analysis
To facilitate the analysis, 23 variables were selected for the model and nine variables were created as a proportion of the total population.

### General quantile variable analysis


Statistic	Min	Pctl(25)	Median	Mean	Pctl(75)	Max

mhmval90	0	58,800	86,500	112,399	141,800	500,001
mhmval00	0	81,600	119,900	144,738	173,894	1,000,001
hinc00	2,499	33,000	43,825	47,657	58,036	200,001
hu00	0	1,102	1,519	1,570	1,999	11,522
own00	0	542	902	939	1,289	4,911
rent00	0	195	398	516	712	8,544
empclf00	0	1,205	1,756	1,820	2,373	10,334
clf00	0	1,302	1,865	1,930	2,502	11,251
unemp00	0	51	87	110	140	6,405
prof00	0	299	539	637	873	6,610
dpov00	0	2,671	3,718	3,804	4,871	23,892
npov00	0	149	304	452	601	5,515
ag25up00	0	1,763	2,451	2,520	3,224	17,974
hs00	0	665	1,071	1,155	1,552	8,909
col00	0	243	492	665	923	9,313
pop00.x	0	2,751	3,802	3,901	4,976	36,206
nhwht00	0	1,308	2,514	2,591	3,713	20,619
nhblk00	0	41	141	522	527	14,039
hisp00	0	55	153	547	533	13,391
asian00	0	22	65	189	183	9,491
p.white	0	47	78	67	91	100
p.black	0	1	4	14	14	100
p.hisp	0	2	4	13	15	100
p.asian	0	1	2	5	5	95
p.hs	0	67	72	72	77	100
p.col	0	12	21	26	36	100
p.prof	0	23	31	34	43	100
p.unemp	0	3	5	6	8	100
pov.rate	0	4	9	12	17	100

__, _ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , _ and _


Statistic	Min	Pctl(25)	Median	Mean	Pctl(75)	Max

mhmval90	0	58,800	86,500	112,399	141,800	500,001
mhmval00	0	81,600	119,900	144,738	173,894	1,000,001
hinc00	2,499	33,000	43,825	47,657	58,036	200,001
hu00	0	1,102	1,519	1,570	1,999	11,522
own00	0	542	902	939	1,289	4,911
rent00	0	195	398	516	712	8,544
empclf00	0	1,205	1,756	1,820	2,373	10,334
clf00	0	1,302	1,865	1,930	2,502	11,251
unemp00	0	51	87	110	140	6,405
prof00	0	299	539	637	873	6,610
dpov00	0	2,671	3,718	3,804	4,871	23,892
npov00	0	149	304	452	601	5,515
ag25up00	0	1,763	2,451	2,520	3,224	17,974
hs00	0	665	1,071	1,155	1,552	8,909
col00	0	243	492	665	923	9,313
pop00.x	0	2,751	3,802	3,901	4,976	36,206
nhwht00	0	1,308	2,514	2,591	3,713	20,619
nhblk00	0	41	141	522	527	14,039
hisp00	0	55	153	547	533	13,391
asian00	0	22	65	189	183	9,491
p.white	0	47	78	67	91	100
p.black	0	1	4	14	14	100
p.hisp	0	2	4	13	15	100
p.asian	0	1	2	5	5	95
p.hs	0	67	72	72	77	100
p.col	0	12	21	26	36	100
p.prof	0	23	31	34	43	100
p.unemp	0	3	5	6	8	100
pov.rate	0	4	9	12	17	100

_ ### Exploration of Median Home Value Below is a statistical description of the 1990 & 2000 MHV, corrected for inflation.


Statistic	Min	Pctl(25)	Median	Mean	Pctl(75)	Max

MedianHomeValue1990	0	77,256	113,651	147,679	186,308	656,941
MedianHomeValue2000	0	81,600	119,900	144,738	173,894	1,000,001
Change.90.to.00	-656,941	-22,553	882	-2,941	19,580	1,000,001

__, _ , , , _ and _


Statistic	Min	Pctl(25)	Median	Mean	Pctl(75)	Max

MedianHomeValue1990	0	77,256	113,651	147,679	186,308	656,941
MedianHomeValue2000	0	81,600	119,900	144,738	173,894	1,000,001
Change.90.to.00	-656,941	-22,553	882	-2,941	19,580	1,000,001

_
## Histogram of the MHV
The historgram below shows the median home value increased $882 from 1990 to 2000, & the mean increase was $2,941.

## Compare the MHV distribution of 1990 to 2000

## Change in MHV 1990-2000

It’s necessary to look at the percentage of change of the house values, not just the amount of median home value increases to contextualize the amount of growth in a given census tract. Additionally, tracts with an mean home value of less the $10k initially but increase to the normal distribution are problematic because they skew the distribution. Additionally, without more data than we have available, it’s not possible to know if those values are accurate. The best practice at this stage is to determine how many tracts are affected and filter them out of the dataset.

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## -1.00000 -0.15550  0.00904  0.05509  0.20782 28.45915      145

## [1] 26

Table continues below
tractid	mhmval90	mhmval00	hinc00	hu00	own00	rent00
fips-01-097-000402	67489	593427	5714	719	7.005	683
fips-04-019-000100	73036	625000	9464	455	18	392
fips-12-099-002600	63800	625000	21944	144	5	124
fips-13-121-002100	32500	261100	14363	799	54	698
fips-17-031-842200	21602	281776	24702	1251	58	757
fips-17-167-001400	55000	1e+06	12321	667	8	524

Table continues below
empclf00	clf00	unemp00	prof00	dpov00	npov00	ag25up00	hs00
260	449	189	17	1953	1575	577	483
250	295	45	96	615	301	553	312
124	136	12	17	419	163	313	236
643	758	115	270	1604	699	1053	501
591	881	290	193	2922	1697	1192	770
257	257	0	117	580	182	764	551

Table continues below
col00	pop00.x	nhwht00	nhblk00	hisp00	asian00	cbsa
6.001	2092	7	2070	13	1	33660
114	605	390	42	143	9	46060
18	414	254	120	36	2	48424
298	1573	317	1073	38	134	12060
193	2783	173	2524	72	10	16974
125	928	661	230	11	21	44100

Table continues below
cbsaname	p.white	p.black	p.hisp	p.asian	p.hs
Mobile, AL	0.3346	98.95	0.6214	0.04781	84.75
Tucson, AZ	64.46	6.942	23.64	1.488	77.03
West Palm Beach-Boca Raton-Boynton FL	61.35	28.99	8.696	0.4831	81.15
Atlanta-Sandy Springs-Marietta, GA	20.15	68.21	2.416	8.519	75.88
Chicago-Naperville-Joliet, IL	6.216	90.69	2.587	0.3593	80.79
Springfield, IL	71.23	24.78	1.185	2.263	88.48

p.col	p.prof	p.unemp	pov.rate
1.04	6.539	42.09	80.64
20.61	38.4	15.25	48.94
5.751	13.71	8.824	38.9
28.3	41.99	15.17	43.58
16.19	32.66	32.92	58.08
16.36	45.53	0	31.38

A total of 26 tracts had an increase of more than 500% between 1990 & 2000. These are the observations that will be filtered out of the analysis.

Plot the percent change variable

## Group the Growth Rates by Metro Area

cbsaname	ave.change.d	growth
Corvallis, OR	$73,784	76.24
Portland-Vancouver-Beaverton, OR-WA	$68,742	72.6
Salt Lake City, UT	$61,688	69.81
Boulder, CO	$95,086	69.34
Provo-Orem, UT	$60,623	68.62
Salem, OR	$49,059	62.02
Eugene-Springfield, OR	$51,452	61.87
Fort Collins-Loveland, CO	$57,614	57.04
Longview, WA	$41,105	55.78
Missoula, MT	$45,872	55.07
Jackson, MI	$28,460	54.13
Greeley, CO	$41,882	52.39
Wenatchee, WA	$46,144	51.15
Detroit-Livonia-Dearborn, MI	$29,104	50.07
Yakima, WA	$31,055	50.04
Ogden-Clearfield, UT	$42,624	49.2
Denver-Aurora, CO	$52,596	48.63
Monroe, MI	$42,470	48.06
Bay City, MI	$26,924	47.72
Logan, UT-ID	$35,923	47.11
Eau Claire, WI	$28,048	43.68
Madison, WI	$43,258	42.8
Sioux Falls, SD	$31,411	41.77
Grand Junction, CO	$34,669	41.55
Mount Vernon-Anacortes, WA a	$42,331	41.26

According to Baum-Snow et al. two major risk factors for gentrification are not being met and will lead to the decrease of central city home values between 1990 & 2000:
1. A statistically significant increase in housing the cost at the 90th percentile. 2. Increasing nearby neighborhood rent.

Measuring Gentrification

Selection of Gentrification Variables

The categories for the selected variables included:

Home Value change & growth
Socio-economic status
Employment
Poverty rate
Education status
Race & Ethnicity

type	variables
shared	a15asn
shared	a15blk
shared	a15hsp
shared	a15ntv
shared	a15wht
shared	a18und

Variable Transformations

For the 1990 & 2000 variables listed above, the same transformations were conducted to find the percentages of the variable in the populations & used to measure the percentage of tracts that underwent gentrification.


Statistic	Min	Pctl(25)	Median	Mean	Pctl(75)	Max

mhv.90	11,657	77,519	113,782	148,038	186,571	656,941
mhv.00	0	81,600	119,900	144,738	173,894	1,000,001
mhv.change	-656,941	-22,638	835	-3,432	19,464	963,869
pct.change	-100	-16	1	6	21	2,846
p.white.90	0	64	87	74	95	100
p.black.90	0	1	3	12	10	100
p.hisp.90	0	1	3	10	9	100
p.asian.90	0	0	1	3	3	94
p.hs.edu.90	0	69	74	74	80	100
p.col.edu.90	0	10	18	22	30	100
p.prof.90	0	17	25	27	34	100
p.unemp.90	0	4	5	7	8	64
pov.rate.90	0	4	8	12	16	100
p.white.00	0	47	78	67	91	100
p.black.00	0	1	4	14	14	100
p.hisp.00	0	2	4	13	15	100
p.asian.00	0	1	2	5	5	95
p.hs.edu.00	0	67	72	72	77	100
p.col.edu.00	0	12	21	26	36	100
p.prof.00	0	23	31	34	43	100
p.unemp.00	0	3	5	6	8	100
pov.rate.00	0	4	9	12	17	100
metro.mhv.pct.90	1	20	41	45	68	100
metro.mhv.pct.00	1	20	41	45	68	100
metro.median.pay.90	14,871	28,906	32,457	32,924	35,833	52,374
metro.median.pay.00	23,012	39,457	43,139	45,054	49,522	73,701
metro.mhv.pct.change	-99	-5	0	0	6	99
pay.change	4,930	9,775	11,441	12,130	14,001	26,211
race.change	-100	-12	-5	-8	-2	100
metro.race.rank.90	1	20	41	45	68	100

__, _ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , _ and _


Statistic	Min	Pctl(25)	Median	Mean	Pctl(75)	Max

mhv.90	11,657	77,519	113,782	148,038	186,571	656,941
mhv.00	0	81,600	119,900	144,738	173,894	1,000,001
mhv.change	-656,941	-22,638	835	-3,432	19,464	963,869
pct.change	-100	-16	1	6	21	2,846
p.white.90	0	64	87	74	95	100
p.black.90	0	1	3	12	10	100
p.hisp.90	0	1	3	10	9	100
p.asian.90	0	0	1	3	3	94
p.hs.edu.90	0	69	74	74	80	100
p.col.edu.90	0	10	18	22	30	100
p.prof.90	0	17	25	27	34	100
p.unemp.90	0	4	5	7	8	64
pov.rate.90	0	4	8	12	16	100
p.white.00	0	47	78	67	91	100
p.black.00	0	1	4	14	14	100
p.hisp.00	0	2	4	13	15	100
p.asian.00	0	1	2	5	5	95
p.hs.edu.00	0	67	72	72	77	100
p.col.edu.00	0	12	21	26	36	100
p.prof.00	0	23	31	34	43	100
p.unemp.00	0	3	5	6	8	100
pov.rate.00	0	4	9	12	17	100
metro.mhv.pct.90	1	20	41	45	68	100
metro.mhv.pct.00	1	20	41	45	68	100
metro.median.pay.90	14,871	28,906	32,457	32,924	35,833	52,374
metro.median.pay.00	23,012	39,457	43,139	45,054	49,522	73,701
metro.mhv.pct.change	-99	-5	0	0	6	99
pay.change	4,930	9,775	11,441	12,130	14,001	26,211
race.change	-100	-12	-5	-8	-2	100
metro.race.rank.90	1	20	41	45	68	100

_

Operationalizing Gentrification

The indicators for gentrification: 1. Metro tracts with home values that were less than the average 2. Metro tracts with higher levels of diversity than the average 3. Metro tracts with home values that incresed more than the overall city gains. 4. Metro tracts with faster growth than the national average of 25% 5. Metro tracts that had an increase of white residents >3%
Utilizing this model, we identified 377 of 17,560 (or 2.15%) tracts that fit the criteria for advanced gentrification.

## [1] 377

## [1] 17560

## [1] 0.02146925

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
-26395	-18.15	1.059	-6.082	17.35	100	198

Discussion

The 377 tracts that were identified as experiencing advanced gentrification experienced drastic economic changes and demographic changes which likely led to a cultural change in the communities.

Spatial Visualization

##         GEOID  POP statea countya tracta pnhwht12 pnhblk12 phisp12 pntv12
## 1 41005020302 4448     41     005 020302    82.47     0.90    6.72   0.00
## 2 41005020303 5107     41     005 020303    88.70     0.40    3.43   0.00
## 3 41005020304 5651     41     005 020304    78.93     0.56    3.24   0.00
## 4 41005020401 6127     41     005 020401    83.86     0.15   11.25   0.94
## 5 41005020403 4223     41     005 020403    92.80     0.94    1.48   0.00
## 6 41005020404 3971     41     005 020404    90.94     0.85    5.92   0.00
##   pasian12 phaw12 pindia12 pchina12 pfilip12 pjapan12 pkorea12 pviet12 p15wht12
## 1     9.03      0     0.00     2.04     0.00     1.40     0.50    5.75    14.32
## 2     4.72      0     0.17     0.00     0.00     0.27     3.80    0.00    14.11
## 3    15.95      0     3.35     8.45     0.56     0.85     1.30    0.00    22.27
## 4     2.16      0     0.00     0.48     0.23     0.64     0.04    0.00    15.75
## 5     1.69      0     0.47     0.86     0.00     0.29     0.08    0.00    22.95
## 6     2.05      0     0.00     0.15     2.72     0.15     0.00    0.00    14.18
##   p65wht12 p15blk12 p65blk12 p15hsp12 p65hsp12 p15ntv12 p65ntv12 p15asn12
## 1    10.30    38.24     0.00    40.16     0.00 13.60773 11.37886    31.97
## 2    17.65     0.00    61.90     0.00     6.74 13.60773 11.37886    10.20
## 3     7.78    25.00     0.00     0.00    11.23 13.60773 11.37886    36.02
## 4    14.73     0.00     0.00    49.72     0.00  0.00000 35.56000     4.31
## 5    12.14    22.22    38.89    21.05     0.00 41.38000  0.00000     0.00
## 6    27.96   100.00     0.00    20.92     0.00 13.60773 11.37886    32.00
##   p65asn12 pmex12 pcuban12 ppr12 pruanc12 pitanc12 pgeanc12 piranc12 pscanc12
## 1    18.31   6.46     0.00     0     1.38     7.31    11.91     5.96     5.82
## 2     0.00   2.66     0.00     0     0.98     2.43    15.43     7.15     6.79
## 3     5.88   1.93     0.00     0     0.21     2.31    11.37     8.75     6.02
## 4     0.00   6.49     0.71     0     0.60     1.93    13.99     7.80     8.34
## 5    27.69   0.26     0.00     0     0.00     2.00    11.42    12.54     4.63
## 6     0.00   5.10     0.00     0     1.00     4.50    14.41     8.06     7.52
##   pfb12 pnat12 p10imm12 prufb12 pitfb12 pgefb12 pirfb12 pscfb12 polang12 plep12
## 1 11.36   8.31     1.06    0.50    0.00    0.48       0    0.24    18.28   1.50
## 2 12.90   8.35     3.88    0.00    0.00    0.73       0    0.00    18.90   2.69
## 3 16.33  10.58     7.43    0.21    0.26    0.38       0    0.23    19.96   5.04
## 4  7.78   4.01     1.81    0.00    0.00    0.37       0    0.00    11.79   1.84
## 5  7.05   5.05     2.52    0.44    0.00    0.21       0    0.00     8.45   1.13
## 6  6.01   4.77     0.00    0.00    0.00    0.54       0    0.00     5.58   0.65
##   phs12 pcol12 punemp12 pflabf12 pprof12 pmanuf12 psemp12 pvet12 p65pov12
## 1 14.34  49.38    12.95    68.95   56.94    10.87   22.60   7.20     0.29
## 2  9.22  67.86     8.83    51.24   60.43     8.72   12.06  10.34     0.48
## 3 12.73  65.82    10.88    65.94   57.71    11.36   11.69   8.80     0.00
## 4 21.32  53.97    12.58    63.51   54.64     8.61   22.10   6.93     0.90
## 5  6.95  74.09     8.95    58.17   66.92    11.22   15.13   9.67     0.00
## 6 10.09  60.67     7.54    46.97   57.20    13.04   23.17  13.51     0.36
##   ppov12 pwpov12 pnapov12 pfmpov12 pbpov12 phpov12 papov12 pvac12 pown12
## 1   5.27    6.35 22.60183     0.00       0    0.39    0.00   5.18  59.72
## 2   8.66    7.78 22.60183     4.64       0   13.86   20.82  10.84  53.29
## 3   7.70    8.14 22.60183     5.34       0    7.49    6.44   1.19  68.70
## 4   6.36    7.46  4.44000     1.33       0    0.55    0.00   3.63  75.69
## 5   8.23    7.83  0.00000     4.95       0    1.75   55.38   1.56  94.26
## 6   2.39    2.62 22.60183     1.04       0    0.00    0.00   3.35  90.22
##   pmulti12 p30old12 p18und12 p60up12 p75up12 pmar12 pwds12 pfhh12 p10yrs12
## 1    27.41    52.35    21.95   14.14    5.32  55.74  18.50   5.03    60.80
## 2    49.45    46.80    17.95   26.19   10.53  48.19  22.08  11.54    75.60
## 3    30.40    13.86    29.20   16.69    1.89  65.48   9.53   3.37    66.56
## 4     9.62    77.59    22.58   21.19    5.43  55.92  19.62   8.65    67.29
## 5     0.00    48.48    28.45   18.99    3.20  70.22   8.04   4.95    58.84
## 6     4.20    77.68    21.11   35.46    8.22  62.38  20.75   4.79    52.45
##   ageblk12 agentv12 agewht12 agehsp12 india12 filip12 japan12 korea12 viet12
## 1       34        0     3115      254       0       0      53      19    217
## 2       21        0     4600      178       9       0      14     197      0
## 3       32        0     4549      187     193      32      49      75      0
## 4        7       45     4033      541       0      11      31       2      0
## 5       36       29     3568       57      18       0      11       3      0
## 6       28        0     3011      196       0      90       5       0      0
##   pop12 nhwht12 nhblk12 ntv12 hisp12 asian12 haw12 china12 a15wht12 a65wht12
## 1  3777    3115      34     0    254     341     0      77      446      321
## 2  5186    4600      21     0    178     245     0       0      649      812
## 3  5763    4549      32     0    187     919     0     487     1013      354
## 4  4809    4033       7    45    541     104     0      23      635      594
## 5  3845    3568      36     0     57      65     0      33      819      433
## 6  3311    3011      28     0    196      68     0       5      427      842
##   a15blk12 a65blk12 a15hsp12 a65hsp12 a15ntv12 a65ntv12 ageasn12 a15asn12
## 1       13        0      102        0        0        0      366      117
## 2        0       13        0       12        0        0      245       25
## 3        8        0        0       21        0        0      919      331
## 4        0        0      269        0        0       16      116        5
## 5        8       14       12        0       12        0       65        0
## 6       28        0       41        0        0        0      100       32
##   a65asn12 mex12 pr12 cuban12 geanc12 iranc12 itanc12 ruanc12 fb12 nat12 itfb12
## 1       67   244    0       0     450     225     276      52  429   314      0
## 2        0   138    0       0     800     371     126      51  669   433      0
## 3       54   111    0       0     655     504     133      12  941   610     15
## 4        0   312    0      34     673     375      93      29  374   193      0
## 5       18    10    0       0     439     482      77       0  271   194      0
## 6        0   169    0       0     477     267     149      33  199   158      0
##   rufb12 ag5up12 irfb12 gefb12 scanc12 n10imm12 olang12 lep12 scfb12 ag25up12
## 1     19    3660      0     18     220       40     669    55      9     2426
## 2      0    5020      0     38     352      201     949   135      0     3914
## 3     12    5475      0     22     347      428    1093   276     13     3809
## 4      0    4562      0     18     401       87     538    84      0     3372
## 5     17    3633      0      8     178       97     307    41      0     2547
## 6      0    3224      0     18     249        0     180    21      0     2517
##   dfmpov12 hh12 hinc12   hincb12 hincw12 hinch12 incpc12 ag18cv12 vet12
## 1      954 1574  72672  49940.72   66932   74506   36643     2932   211
## 2     1335 2492  72500  19519.00   73177   64034   45204     4255   440
## 3     1574 2246  92572 250001.00   89219   81317   47287     4080   359
## 4     1283 2073  62879  49940.72   62331  141023   40156     3723   258
## 5     1112 1324 135326  49940.72  135163  165588   57300     2751   266
## 6      961 1472  92461  49940.72   93333   53056   56517     2612   353
##   empclf12 dpov12 npov12 dbpov12 nbpov12 dnapov12 nnapov12 dwpov12 nwpov12
## 1     1960   3759    198      31       0        0        0    3100     197
## 2     2570   5163    447      21       0        0        0    4589     357
## 3     2807   5740    442      32       0        0        0    4545     370
## 4     2403   4799    305       7       0       45        2    4023     300
## 5     1738   3841    316      36       0       29        0    3564     279
## 6     1472   3311     79      28       0        0        0    3011      79
##   dhpov12 nhpov12 hhb12 hhw12 hhh12 hs12 col12 clf12 unemp12 dflabf12 flabf12
## 1     254       1     0  1418    69  348  1198  2240     290     1636    1128
## 2     166      23    21  2209    57  361  2656  2741     242     2457    1259
## 3     187      14    13  1897    87  485  2507  3088     336     2234    1473
## 4     541       3     0  1884   115  719  1820  2695     339     1913    1215
## 5      57       1    14  1235    44  177  1887  1821     163     1444     840
## 6     196       0     0  1440    32  254  1527  1512     114     1401     658
##   prof12 manuf12 semp12 hha12   hinca12 n65pov12 nfmpov12 napov12 dapov12
## 1   1116     213    443    76 100313.00       11        0       0     366
## 2   1553     224    310    98  75400.00       25       62      51     245
## 3   1620     319    328   237 159375.00        0       84      58     900
## 4   1313     207    531    56  83462.00       43       17       0     116
## 5   1163     195    263    21  12386.00        0       55      36      65
## 6    842     192    341     0  79531.26       12       10       0     100
##   family12 hu12 vac12 ohu12 own12 rent12 dmulti12 mrent12 mhmval12 multi12
## 1      954 1660    86  1574   940    634     1660     950   407000     455
## 2     1335 2795   303  2492  1328   1164     2795    1037   407300    1382
## 3     1574 2273    27  2246  1543    703     2273     967   509400     691
## 4     1283 2151    78  2073  1569    504     2151    1045   369500     207
## 5     1112 1345    21  1324  1248     76     1345    2001   668900       0
## 6      961 1523    51  1472  1328    144     1523     939   486100      64
##   h30old12 h10yrs12 a18und12 a60up12 a75up12 ag15up12 X12.Mar wds12 fhh12
## 1      869      957      829     534     201     3102    1729   574    48
## 2     1308     1884      931    1358     546     4501    2169   994   154
## 3      315     1495     1683     962     109     4398    2880   419    53
## 4     1669     1395     1086    1019     261     3859    2158   757   111
## 5      652      779     1094     730     123     2948    2070   237    55
## 6     1183      772      699    1174     272     2815    1756   584    46
##       pop.w cluster
## 1 0.4942222       3
## 2 0.5674444       1
## 3 0.6278889       1
## 4 0.6807778       1
## 5 0.4692222       1
## 6 0.4412222       1

## [1] 412

## Variable(s) "pct.change" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

## Geographical Distribution of Median Home Values
The highest valued home are located south and west of Portland’s downtown area, with a smaller numner in the south east and north east corner. Interestingly, the biggest changes in median home values occurred closer to the downtown areas and throughout the south eastern tracts. However, the loss in median home values was concentrated the most in the northern tracts.