Last modified August 26, 2021

#load processed survey data
load("C:/Users/court/Google Drive/Research/Seattle Rental Housing Study/SRHS/landlord_survey_w_weights.RData")

# keep original data just in case

LANDLORD SIZE

To get a better picture of how landlord size might reflect corporatization, there is a new categorization of landlord size:

Landlords with:

  • Many buildings & at least 1 large building (20+ units)
  • Many buildings & no large buildings
  • Only 1 large building
  • Only 1 building, not large

These categories were then crosstabbed with other variables resulting in 4 tables below:

  • landlord size by landlord characteristics
  • landlord size by business practices
  • landlord size by policy attitudes
  • landlord size by policy adaptation

Most landlords do not have large buildings, but instead 60% have units within one small building and 35% have units within several small buildings (see below).

svydesign(ids = ~ 1, data = land, weights = land$rrio_wt) %>%
  tbl_svysummary(
    by = ll_large,
    percent = "row",
    missing = "no",
    include = c(temp_N, ll_large),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
  add_overall(col_label = "**Overall**", last = TRUE) %>%
  modify_spanning_header(starts_with("stat_") ~ "**Landlord size (weighted)**")
Characteristic Landlord size (weighted) p-value
Many bldgs & large bldgs1 Many bldgs & no large bldgs1 One bldg, large1 One bldg, not large1 Overall1
Total 117 (3.0%) 1,337 (35%) 77 (2.0%) 2,301 (60%) 3,831 (100%)
1 n (%)




LANDLORD CHARACTERISTICS

  • columns add up to 100%
  • strangely, there seems to be a chunk of respondents who reported one unit, yet more than one building
# WEIGHTED tables for landlord size by characteristics
svydesign(ids = ~ 1, data = land, weights = land$rrio_wt) %>%
  tbl_svysummary(
    by = ll_large,
    percent = "col",
    missing = "no",
    include = c(total_units, tenure, respondent_role, financial_role, temp_N, ll_large),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
  add_overall(col_label = "**Overall**", last = TRUE) %>%
  modify_spanning_header(starts_with("stat_") ~ "**Landlord size and landlord characteristics (weighted)**")
Characteristic Landlord size and landlord characteristics (weighted) p-value2
Many bldgs & large bldgs1 Many bldgs & no large bldgs1 One bldg, large1 One bldg, not large1 Overall1
total_units <0.001
    1 unit 1 (0.8%) 136 (10%) 14 (18%) 1,861 (81%) 2,012 (53%)
    2-4 units 7 (6.3%) 832 (62%) 3 (3.8%) 376 (16%) 1,219 (32%)
    5-19 units 91 (78%) 235 (18%) 60 (78%) 29 (1.2%) 415 (11%)
    20-49 units 13 (11%) 56 (4.2%) 0 (0%) 15 (0.6%) 83 (2.2%)
    50+ units 5 (4.0%) 77 (5.8%) 0 (0%) 21 (0.9%) 103 (2.7%)
tenure <0.001
    0-2 yrs 0 (0%) 26 (1.9%) 2 (2.5%) 139 (6.1%) 167 (4.4%)
    2-4 yrs 23 (20%) 365 (27%) 21 (27%) 631 (28%) 1,039 (27%)
    5-9 yrs 6 (5.1%) 117 (8.8%) 2 (2.6%) 447 (20%) 572 (15%)
    10-19 yrs 79 (68%) 595 (45%) 40 (53%) 523 (23%) 1,237 (32%)
    20+ yrs 8 (6.9%) 232 (17%) 11 (15%) 553 (24%) 804 (21%)
respondent role <0.001
    Property Owner 29 (25%) 438 (33%) 28 (37%) 1,092 (47%) 1,587 (41%)
    Property Manager 7 (6.3%) 69 (5.2%) 1 (1.8%) 57 (2.5%) 135 (3.5%)
    Both Property Owner & Property Manager 81 (69%) 830 (62%) 47 (62%) 1,152 (50%) 2,109 (55%)
financial role <0.001
    Multiple 37 (32%) 390 (29%) 21 (27%) 498 (22%) 946 (25%)
    Other 3 (2.3%) 37 (2.8%) 2 (2.3%) 131 (5.7%) 172 (4.5%)
    Primary only 29 (25%) 200 (15%) 9 (11%) 122 (5.3%) 360 (9.4%)
    Retirment only 25 (22%) 309 (23%) 26 (33%) 688 (30%) 1,048 (27%)
    Supplementary only 22 (19%) 396 (30%) 20 (26%) 853 (37%) 1,291 (34%)
Total 117 (100%) 1,337 (100%) 77 (100%) 2,301 (100%) 3,831 (100%)
1 n (%)
2 chi-squared test with Rao & Scott's second-order correction




BUSINESS PRACTICES

# Create tables, weighted then unweighted

# WEIGHTED tables for landlord size and business practices
svydesign(ids = ~ 1, data = land, weights = land$rrio_wt) %>%
  tbl_svysummary(
    by = ll_large,
    percent = "col",
    missing = "no",
    include = c(tenant_income, 
                rent_screen, 
                flex_decision, 
                rent_voucher, 
                rent_monthly,
                        rent_raise,
                        terminate_num,
                        terminate_reason_pay,
                        terminate_reason_paylate,                        
                        terminate_reason_voucher,
                        terminate_reason_rules ,
                        terminate_reason_occupy ,
                        terminate_reason_sell, 
                        terminate_reason_other,
                        terminate_court, 
                temp_N, ll_large),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
  add_overall(col_label = "**Overall**", last = TRUE) %>%
  modify_spanning_header(starts_with("stat_") ~ "**Landlord size by business practices (weighted)**")
Characteristic Landlord size by business practices (weighted) p-value2
Many bldgs & large bldgs1 Many bldgs & no large bldgs1 One bldg, large1 One bldg, not large1 Overall1
tenant HH income <0.001
    Less than $25,000 per year 3 (2.5%) 56 (4.2%) 8 (9.8%) 109 (4.7%) 175 (4.6%)
    $25,000-50,000 per year 51 (44%) 315 (24%) 22 (28%) 398 (17%) 785 (21%)
    $50,000-75,000 per year 38 (32%) 411 (31%) 24 (32%) 577 (25%) 1,050 (27%)
    $75,000-150,000 per year 15 (13%) 394 (29%) 10 (13%) 808 (35%) 1,227 (32%)
    More than $150,000 per year 2 (1.4%) 65 (4.8%) 2 (2.7%) 225 (9.8%) 294 (7.7%)
    Refused 9 (7.4%) 97 (7.2%) 11 (15%) 184 (8.0%) 300 (7.8%)
third party screener <0.001
    Yes 53 (46%) 590 (44%) 32 (41%) 891 (39%) 1,566 (41%)
    No 59 (51%) 733 (55%) 45 (59%) 1,337 (58%) 2,174 (57%)
    Refused 4 (3.2%) 14 (1.0%) 0 (0%) 73 (3.2%) 90 (2.4%)
flexible leasing decisions <0.001
    Strongly agree 9 (7.6%) 193 (14%) 7 (8.5%) 225 (9.8%) 433 (11%)
    Agree 50 (43%) 576 (43%) 32 (42%) 926 (40%) 1,585 (41%)
    Neither agree nor disagree 25 (21%) 267 (20%) 21 (27%) 515 (22%) 827 (22%)
    Disagree 17 (14%) 186 (14%) 12 (16%) 430 (19%) 645 (17%)
    Strongly disagree 12 (10%) 99 (7.4%) 5 (6.8%) 174 (7.6%) 291 (7.6%)
    Refused 4 (3.3%) 16 (1.2%) 0 (0%) 30 (1.3%) 50 (1.3%)
rent to voucher holder <0.001
    Yes 58 (50%) 342 (26%) 19 (24%) 216 (9.4%) 635 (17%)
    No 48 (41%) 919 (69%) 53 (68%) 1,962 (85%) 2,981 (78%)
    Don't know 11 (9.6%) 70 (5.3%) 6 (7.4%) 111 (4.8%) 198 (5.2%)
    Refused 0 (0%) 6 (0.4%) 0 (0%) 11 (0.5%) 17 (0.5%)
avg. monthly rent <0.001
    Less than $500 0 (0%) 2 (0.2%) 0 (0%) 11 (0.5%) 13 (0.4%)
    $500-1,000 20 (17%) 86 (6.6%) 6 (8.4%) 114 (5.4%) 226 (6.3%)
    $1,001-1,500 53 (46%) 312 (24%) 36 (49%) 432 (21%) 834 (23%)
    $1,501-2,500 39 (33%) 615 (47%) 28 (38%) 1,035 (49%) 1,718 (48%)
    $2,501-3,500 3 (2.7%) 227 (17%) 2 (2.8%) 392 (19%) 624 (17%)
    $3,501 or more 0 (0.4%) 43 (3.3%) 0 (0%) 90 (4.3%) 134 (3.7%)
    Refused 1 (0.9%) 21 (1.6%) 1 (2.0%) 26 (1.2%) 50 (1.4%)
amount rent raise last yr <0.001
    0% (I have not raised the rent in a Seattle unit in the past year) 21 (18%) 385 (29%) 14 (18%) 1,021 (44%) 1,441 (38%)
    1-5% 35 (30%) 465 (35%) 23 (30%) 654 (28%) 1,177 (31%)
    6-10% 48 (41%) 351 (26%) 33 (42%) 376 (16%) 808 (21%)
    11-15% 6 (4.7%) 64 (4.8%) 4 (5.7%) 90 (3.9%) 163 (4.3%)
    16-25% 2 (2.0%) 34 (2.6%) 2 (2.1%) 80 (3.5%) 118 (3.1%)
    More than 25% 3 (2.2%) 24 (1.8%) 0 (0%) 42 (1.8%) 68 (1.8%)
    Refused 3 (2.2%) 14 (1.0%) 1 (1.0%) 38 (1.6%) 54 (1.4%)
Num of tenancies terminated last yr
    0 93 (80%) 1,135 (85%) 69 (89%) 2,121 (92%) 3,418 (89%)
    1-5 22 (19%) 172 (13%) 7 (8.8%) 130 (5.7%) 331 (8.6%)
    6-10 0 (0%) 8 (0.6%) 0 (0%) 2 (0.1%) 10 (0.3%)
    11-20 0 (0%) 3 (0.2%) 0 (0%) 0 (0%) 3 (<0.1%)
    More than 20 0 (0%) 2 (0.2%) 0 (0%) 1 (<0.1%) 3 (<0.1%)
    Don't know 2 (1.8%) 11 (0.8%) 1 (0.9%) 26 (1.1%) 40 (1.1%)
    Refused 0 (0%) 6 (0.5%) 1 (1.0%) 19 (0.8%) 26 (0.7%)
terminate - failed to pay rent/fees 0.87
    Tenant(s) failed to pay rent or fees 12 (93%) 89 (91%) 2 (100%) 49 (88%) 152 (90%)
    Refused 1 (6.8%) 9 (9.4%) 0 (0%) 7 (12%) 17 (10%)
terminate - paid late rent/fees 0.78
    Tenant(s) consistently paid rent/fees late 3 (77%) 49 (84%) 4 (100%) 29 (81%) 85 (83%)
    Refused 1 (23%) 9 (16%) 0 (0%) 7 (19%) 17 (17%)
terminate - lost housing voucher 0.22
    Tenant lost their housing voucher 1 (57%) 5 (33%) 0 (NA%) 0 (0%) 6 (25%)
    Refused 1 (43%) 9 (67%) 0 (NA%) 7 (100%) 17 (75%)
terminate - didn't comply with rules 0.85
    Tenant(s) failed to comply with rules of rental agreement (other than regarding rent) 11 (92%) 86 (90%) 4 (100%) 50 (88%) 151 (90%)
    Refused 1 (7.8%) 9 (9.7%) 0 (0%) 7 (12%) 17 (10%)
terminate - R to occupy unit 0.59
    So that you or a family member could occupy the unit 1 (44%) 10 (53%) 1 (100%) 14 (68%) 26 (60%)
    Refused 1 (56%) 9 (47%) 0 (0%) 7 (32%) 17 (40%)
terminate - R to sell unit 0.86
    So that you could sell the unit 3 (77%) 22 (71%) 1 (100%) 24 (78%) 50 (74%)
    Refused 1 (23%) 9 (29%) 0 (0%) 7 (22%) 17 (26%)
terminate - other 0.99
    Other 3 (75%) 38 (80%) 0 (NA%) 24 (78%) 65 (79%)
    Refused 1 (25%) 9 (20%) 0 (NA%) 7 (22%) 17 (21%)
terminate through courts 0.021
    Always directly with the tenant 9 (43%) 98 (53%) 5 (68%) 98 (73%) 210 (61%)
    Through the courts and directly with the tenant, but more often directly with the tenant 6 (26%) 42 (23%) 1 (17%) 17 (12%) 65 (19%)
    Through the courts and directly with the tenant, but more often through the courts 5 (22%) 20 (11%) 1 (14%) 6 (4.4%) 31 (9.0%)
    Always through the courts 1 (4.2%) 9 (5.0%) 0 (0%) 3 (2.1%) 13 (3.7%)
    Refused 1 (4.1%) 16 (8.7%) 0 (0%) 10 (7.8%) 27 (7.9%)
Total 117 (100%) 1,337 (100%) 77 (100%) 2,301 (100%) 3,831 (100%)
1 n (%)
2 chi-squared test with Rao & Scott's second-order correction




POLICY ATTITUDES

# Create tables, weighted then unweighted

# WEIGHTED tables for landlord size and policy attitudes
svydesign(ids = ~ 1, data = land, weights = land$rrio_wt) %>%
  tbl_svysummary(
    by = ll_large,
    percent = "col",
    missing = "no",
    include = c(efficacy_caps,
                efficacy_pay_plans,
                efficacy_protect,
                efficacy_first,
                efficacy_fair,
                temp_N, ll_large),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
  add_overall(col_label = "**Overall**", last = TRUE) %>%
  modify_spanning_header(starts_with("stat_") ~ "**Landlord size by policy attitudes (weighted)**")
Characteristic Landlord size by policy attitudes (weighted) p-value2
Many bldgs & large bldgs1 Many bldgs & no large bldgs1 One bldg, large1 One bldg, not large1 Overall1
efficacy of caps on fees/deposits <0.001
    Very effective 0 (0%) 26 (1.9%) 1 (1.2%) 36 (1.6%) 63 (1.6%)
    Effective 7 (6.1%) 183 (14%) 10 (13%) 432 (19%) 631 (16%)
    Neither effective nor ineffective 37 (32%) 379 (28%) 23 (30%) 585 (25%) 1,025 (27%)
    Ineffective 34 (29%) 334 (25%) 21 (27%) 540 (23%) 930 (24%)
    Very ineffective 33 (28%) 314 (23%) 16 (21%) 381 (17%) 744 (19%)
    I don't know enough about this ordinance to respond 4 (3.5%) 94 (7.0%) 5 (6.3%) 312 (14%) 415 (11%)
    Refused 1 (1.1%) 6 (0.5%) 1 (1.7%) 15 (0.7%) 24 (0.6%)
efficacy of payment plans for fees/deposits <0.001
    Very effective 2 (2.0%) 42 (3.1%) 2 (2.9%) 77 (3.4%) 123 (3.2%)
    Effective 26 (23%) 365 (27%) 22 (29%) 697 (30%) 1,111 (29%)
    Neither effective nor ineffective 38 (33%) 347 (26%) 23 (30%) 575 (25%) 982 (26%)
    Ineffective 22 (19%) 267 (20%) 15 (20%) 393 (17%) 698 (18%)
    Very ineffective 25 (22%) 241 (18%) 11 (14%) 305 (13%) 582 (15%)
    I don't know enough about this ordinance to respond 1 (0.9%) 69 (5.2%) 2 (2.9%) 237 (10%) 309 (8.1%)
    Refused 1 (1.1%) 6 (0.5%) 1 (1.7%) 16 (0.7%) 25 (0.6%)
efficacy of income protection <0.001
    Very effective 1 (1.1%) 26 (2.0%) 1 (1.7%) 34 (1.5%) 63 (1.6%)
    Effective 15 (13%) 263 (20%) 17 (22%) 499 (22%) 795 (21%)
    Neither effective nor ineffective 43 (37%) 447 (33%) 26 (33%) 634 (28%) 1,149 (30%)
    Ineffective 24 (20%) 221 (17%) 13 (17%) 270 (12%) 527 (14%)
    Very ineffective 19 (16%) 141 (11%) 6 (7.4%) 213 (9.3%) 378 (9.9%)
    I don't know enough about this ordinance to respond 15 (13%) 232 (17%) 11 (14%) 629 (27%) 886 (23%)
    Refused 0 (0%) 8 (0.6%) 3 (4.1%) 21 (0.9%) 32 (0.8%)
efficacy of first-in-time <0.001
    Very effective 0 (0%) 14 (1.1%) 0 (0%) 29 (1.3%) 43 (1.1%)
    Effective 8 (7.1%) 109 (8.1%) 4 (5.3%) 264 (11%) 385 (10%)
    Neither effective nor ineffective 29 (25%) 325 (24%) 32 (41%) 634 (28%) 1,019 (27%)
    Ineffective 33 (29%) 347 (26%) 22 (29%) 587 (25%) 989 (26%)
    Very ineffective 44 (38%) 496 (37%) 15 (20%) 600 (26%) 1,155 (30%)
    I don't know enough about this ordinance to respond 1 (0.9%) 41 (3.0%) 2 (2.3%) 172 (7.5%) 216 (5.6%)
    Refused 1 (1.1%) 6 (0.4%) 2 (3.1%) 14 (0.6%) 23 (0.6%)
efficacy of fair chance housing <0.001
    Very effective 0 (0.1%) 28 (2.1%) 2 (3.0%) 44 (1.9%) 75 (2.0%)
    Effective 22 (19%) 261 (19%) 15 (19%) 548 (24%) 845 (22%)
    Neither effective nor ineffective 39 (34%) 385 (29%) 25 (33%) 596 (26%) 1,045 (27%)
    Ineffective 21 (18%) 266 (20%) 17 (22%) 365 (16%) 669 (17%)
    Very ineffective 33 (28%) 239 (18%) 9 (12%) 320 (14%) 601 (16%)
    I don't know enough about this ordinance to respond 2 (1.4%) 147 (11%) 6 (7.9%) 403 (18%) 558 (15%)
    Refused 0 (0%) 11 (0.8%) 3 (4.1%) 24 (1.0%) 38 (1.0%)
Total 117 (100%) 1,337 (100%) 77 (100%) 2,301 (100%) 3,831 (100%)
1 n (%)
2 chi-squared test with Rao & Scott's second-order correction




POLICY ADAPTATION

# Create tables, weighted then unweighted

# WEIGHTED tables for landlord size and policy adaptation
svydesign(ids = ~ 1, data = land, weights = land$rrio_wt) %>%
  tbl_svysummary(
    by = ll_large,
    percent = "col",
    missing = "no",
    include = c(adapt_any,
                adapt_caps,
                adapt_pay_plan,
                adapt_protect,
                adapt_first,
                adapt_fair,
                sold_regulation,
                sold_caps,
                sold_pay_plan,
                sold_protect,
                sold_first,
                sold_fair,
                rent_raise_regulation,
                security_deposit,
                temp_N, ll_large),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
  add_overall(col_label = "**Overall**", last = TRUE) %>%
  modify_spanning_header(starts_with("stat_") ~ "**Landlord size by policy adaptation (weighted)**")
Characteristic Landlord size by policy adaptation (weighted) p-value2
Many bldgs & large bldgs1 Many bldgs & no large bldgs1 One bldg, large1 One bldg, not large1 Overall1
adapt to stricter reqs due to regulations <0.001
    Yes, already adopted 68 (58%) 631 (47%) 28 (37%) 712 (31%) 1,439 (38%)
    No, but plan to 20 (17%) 287 (21%) 19 (24%) 621 (27%) 947 (25%)
    No, no plans 18 (16%) 245 (18%) 15 (19%) 542 (24%) 820 (21%)
    Not sure 10 (8.4%) 159 (12%) 10 (13%) 393 (17%) 572 (15%)
    Refused 1 (0.9%) 15 (1.1%) 5 (6.9%) 33 (1.4%) 54 (1.4%)
adapt caps on fees/deposits 0.10
    No/Refused 0 (0%) 7 (1.2%) 0 (0%) 21 (2.9%) 28 (2.1%)
    Yes 57 (100%) 545 (99%) 22 (100%) 711 (97%) 1,336 (98%)
adapt payment plans for fees/deposits 0.083
    No/Refused 0 (0%) 7 (1.4%) 0 (0%) 21 (3.5%) 28 (2.4%)
    Yes 53 (100%) 470 (99%) 22 (100%) 593 (97%) 1,138 (98%)
adapt income protection 0.13
    No/Refused 0 (0%) 7 (1.7%) 0 (0%) 21 (3.8%) 28 (2.7%)
    Yes 42 (100%) 395 (98%) 17 (100%) 544 (96%) 999 (97%)
adapt first-in-time 0.18
    No/Refused 0 (0%) 7 (0.9%) 0 (0%) 21 (1.8%) 28 (1.3%)
    Yes 80 (100%) 790 (99%) 41 (100%) 1,160 (98%) 2,072 (99%)
adapt fair chance housing 0.16
    No/Refused 0 (0%) 7 (1.0%) 0 (0%) 21 (2.1%) 28 (1.5%)
    Yes 75 (100%) 691 (99%) 34 (100%) 992 (98%) 1,792 (98%)
sell - regulations <0.001
    Definitely yes 31 (26%) 323 (24%) 16 (20%) 268 (12%) 637 (17%)
    Probably yes 31 (26%) 321 (24%) 17 (21%) 479 (21%) 847 (22%)
    Unsure 20 (17%) 245 (18%) 19 (25%) 549 (24%) 833 (22%)
    Probably not 27 (23%) 278 (21%) 13 (17%) 517 (22%) 835 (22%)
    Definitely not 9 (7.5%) 166 (12%) 13 (17%) 473 (21%) 660 (17%)
    Refused 0 (0%) 3 (0.2%) 0 (0%) 15 (0.7%) 19 (0.5%)
sell - caps on fees/deposits 0.33
    No/Refused 2 (4.8%) 5 (1.3%) 0 (0%) 5 (1.2%) 12 (1.4%)
    Yes 33 (95%) 379 (99%) 15 (100%) 422 (99%) 848 (99%)
sell - payment plans for fees/deposits 0.48
    No/Refused 2 (4.8%) 5 (1.6%) 0 (0%) 5 (1.5%) 12 (1.7%)
    Yes 33 (95%) 306 (98%) 12 (100%) 348 (99%) 700 (98%)
sell - income protection 0.36
    No/Refused 2 (6.1%) 5 (1.8%) 0 (0%) 5 (1.6%) 12 (1.8%)
    Yes 26 (94%) 282 (98%) 13 (100%) 320 (98%) 641 (98%)
sell - first-in-time 0.33
    No/Refused 2 (3.3%) 5 (0.9%) 0 (0%) 5 (0.8%) 12 (0.9%)
    Yes 49 (97%) 531 (99%) 26 (100%) 640 (99%) 1,247 (99%)
sell - fair chance housing 0.38
    No/Refused 2 (3.2%) 5 (1.0%) 0 (0%) 5 (0.8%) 12 (1.0%)
    Yes 51 (97%) 520 (99%) 29 (100%) 605 (99%) 1,205 (99%)
rent raised due to regulations 26 (28%) 215 (23%) 10 (17%) 224 (18%) 475 (20%) 0.007
Change security deposit practice <0.001
    No, no change in practices 99 (86%) 1,074 (84%) 61 (81%) 1,566 (84%) 2,800 (84%)
    Yes, less time to return deposit 1 (1.0%) 42 (3.3%) 4 (5.6%) 48 (2.6%) 96 (2.9%)
    Yes, more time to return deposit 11 (9.7%) 86 (6.8%) 4 (5.8%) 54 (2.9%) 155 (4.7%)
    NA, no tenants move before 7/2016 1 (1.2%) 61 (4.8%) 5 (6.6%) 178 (9.5%) 245 (7.3%)
    Refused 3 (2.3%) 9 (0.7%) 1 (1.5%) 23 (1.2%) 35 (1.1%)
Total 117 (100%) 1,337 (100%) 77 (100%) 2,301 (100%) 3,831 (100%)
1 n (%)
2 chi-squared test with Rao & Scott's second-order correction