SRHS Simple Models

coded_df <- readRDS("C:/Users/court/Google Drive/Research/SRHS/SRHS_coded.RDS")

df <- coded_df %>% select(
   ll_finrole2,
   ll_size,
   bp_advert_b,
   bp_screen_b,
   bp_dec_flex_b,
   bp_dec_standard_b,
   bp_dec_offer_b,
   bp_vouch_b,
   bp_crim_b,
   bp_dis_b,
   bp_keep_pct_num,
   bp_keep_rsn,
   bp_install_b,
   bp_comm_unit_fee_app_b,
   bp_comm_unit_fee_sec_b,
   bp_comm_unit_fee_clean_b,
   bp_comm_unit_fee_park_b,
   bp_fair_b,
   bp_comm_unit_fee_incl_b,
   bp_comm_unit_deposit_b,
   bp_rent_raise_b,
   bp_rent_raise_num,
   bp_rent_raise_rising,
   bp_rent_raise_regulation,
   bp_rent_raise_market,
   bp_terminate_b,
   bp_terminate_num,
   bp_terminate_paylate,
   bp_terminate_pay,
   bp_court_b,
   bp_income_b,
   bp_income_num,
   ll_remain_1or5yr_b,
   ll_remain_reg
   )

df <- df %>% mutate(
  ll_size = relevel(as.factor(ll_size), ref="small"),
  ll_finrole2 = as.factor(ll_finrole2)
)

df <- df %>% rename(
                    "Landlord_size_" = ll_size,
                    "Financial_role_" = ll_finrole2,
                    "Advertising" = bp_advert_b,
                    "Screening" = bp_screen_b,
                    "Flexible_decision" = bp_dec_flex_b,
                    "Standard_criteria" = bp_dec_standard_b,
                    "Offers_to_nonstandard" = bp_dec_offer_b,
                    "Rent_to_vouchers" = bp_vouch_b,
                    "Rent_to_criminal" = bp_crim_b,
                    "Disability_accom." = bp_dis_b,
                    "Deposit_pct_kept" = bp_keep_pct_num,
                    "Deposit_kept_reason" = bp_keep_rsn,
                    "Allow_installments" = bp_install_b,
                    "Fair_market_price" = bp_fair_b,
                    "App_fee"   =   bp_comm_unit_fee_app_b,
                    "Security_deposit" =    bp_comm_unit_fee_sec_b,
                    "Cleaning_fee"  =   bp_comm_unit_fee_clean_b,
                    "Parking_fee"   =   bp_comm_unit_fee_park_b,
                    "Utilities_included" = bp_comm_unit_fee_incl_b,
                    "Last_month_required" = bp_comm_unit_deposit_b,
                    "Raised_rent" = bp_rent_raise_b,
                    "Raised_rent_pct" = bp_rent_raise_num,
                    "Raised_rent_rising_costs" = bp_rent_raise_rising,
                    "Raised_rent_regulations" = bp_rent_raise_regulation,
                    "Raised_rent_market" =  bp_rent_raise_market,
                     "Ever_terminated" = bp_terminate_b,
                     "Num_of_terminations" = bp_terminate_num,
                     "Terminated_paid_late" = bp_terminate_paylate,
                     "Terminated_non_payment" = bp_terminate_pay,
                     "Terminated_use_courts" = bp_court_b,
                     "Renterincome_above_avg_Seattle" = bp_income_b,
                     "Renter_income_estimate" = bp_income_num,
                     "Still_mngr_1_5yrs" = ll_remain_1or5yr_b,
                     "Stop_mngr_burdensome_regs" = ll_remain_reg)
                   


# --------------------------
# # Variable groups
# 
# 
# # group A
#                     "Advertising",
#                     "Screening",
#                     "Flexible decision",
#                     "Standard criteria",
#                     "Offers to nonstandard",
#                     "Rent to vouchers",
#                     "Rent to criminal",
#                     "Disability accom.",
#                     
# # group B
#                     "Deposit pct. kept",
#                     "Depsoit kept reason",
#                     "Allow installments",
#                     "Utilities included",
#                     "App fee",
#                     "Security deposit",
#                     "Cleaning fee",
#                     "Parking fee",
# # group C
# 
#                     
#                     "Fair market price",
#                     "Last month required",
#                     "Raised rent",
#                     "Raised rent pct.",
#                     "Raised rent - rising costs",
#                     "Raised rent - regulations",
#                     "Raised rent - market",
#                     
# # group D
#                      "Ever terminated",
#                      "Num of terminations",
#                      "Terminated - paid late",
#                      "Terminated - non payment",
#                      "Terminated - use courts",
#                     
# # group E
#                      "Renter income - above avg. Seattle",
#                      "Renter income estimate",
#                      "Still be prop mngr in 1, 5 yrs",
#                      "Stop being prop mngr burdensome regs"

Business practices, landlord size, and financial role

This document shows simple crosstabs and models for:

each business practice and landlord size
each business practice, landlord size, financial role, and interaction between size*financial

Most variables were forced into a binary (likert scale included) and odds ratios are presented. Where possible, numbers were left numeric.

Cross tab of interaction variables:

df %>% 
  tbl_summary(
    by = Landlord_size_,
    percent = "row",
    missing = "no",
    include = c(Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Landlord size & financial role**")

Characteristic	Landlord size & financial role		p-value¹
Characteristic	small	big	p-value¹
Financial_role_, n (%)			<0.001
Other	2,767 (82%)	594 (18%)
Primary Income	169 (37%)	288 (63%)
¹ Pearson's Chi-squared test

Leasing

Advertising

Outcome:

0: word-of-mouth
1: non word-of-mouth

Crosstabs:

df %>% 
  tbl_summary(
    by = Advertising,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Advertising**")

Characteristic	Advertising		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	2,603 (88%)	343 (12%)
big	856 (96%)	34 (3.8%)
Financial_role_, n (%)			<0.001
Other	2,997 (89%)	364 (11%)
Primary Income	445 (97%)	12 (2.6%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Advertising~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Advertising~1 + Landlord_size_ + Financial_role_ +
            (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.30 (0.21 to 0.43)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate = TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.39 (0.26 to 0.56)	<0.001
Financial_role_
Other	—
Primary Income	0.36 (0.16 to 0.70)	0.006
Landlord_size_ * Financial_role_
big * Primary Income	0.73 (0.18 to 2.52)	0.63
¹ OR = Odds Ratio, CI = Confidence Interval

Screening

Outcome: use of third party screening service

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Screening,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Screening**")

Characteristic	Screening		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	1,810 (61%)	1,136 (39%)
big	433 (49%)	457 (51%)
Financial_role_, n (%)			<0.001
Other	2,001 (60%)	1,360 (40%)
Primary Income	230 (50%)	227 (50%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Screening~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Screening~1 + Landlord_size_ + Financial_role_ +
            (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.68 (1.45 to 1.96)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.60 (1.34 to 1.92)	<0.001
Financial_role_
Other	—
Primary Income	1.13 (0.82 to 1.55)	0.44
Landlord_size_ * Financial_role_
big * Primary Income	1.06 (0.69 to 1.62)	0.80
¹ OR = Odds Ratio, CI = Confidence Interval

Flexible decision

Outcome: criteria used to make leasing decisions is flexible

1: agree
0: disagree or neutral

Crosstabs:

df %>% 
  tbl_summary(
    by = Flexible_decision,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Flexible decision**")

Characteristic	Flexible decision		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.40
small	1,150 (43%)	1,551 (57%)
big	316 (41%)	457 (59%)
Financial_role_, n (%)			0.16
Other	1,311 (43%)	1,762 (57%)
Primary Income	149 (39%)	234 (61%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Flexible_decision~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Flexible_decision~1 + Landlord_size_ + Financial_role_ +
            (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.07 (0.91 to 1.26)	0.40
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.11 (0.91 to 1.34)	0.30
Financial_role_
Other	—
Primary Income	1.42 (1.01 to 2.03)	0.047
Landlord_size_ * Financial_role_
big * Primary Income	0.68 (0.42 to 1.08)	0.10
¹ OR = Odds Ratio, CI = Confidence Interval

Standard criteria

Outcome: decisions based on a standard set of criteria

1: agree
0: disagree or neutral

Crosstabs:

df %>% 
  tbl_summary(
    by = Standard_criteria,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Standard criteria**")

Characteristic	Standard criteria		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	839 (29%)	2,075 (71%)
big	179 (20%)	706 (80%)
Financial_role_, n (%)			<0.001
Other	924 (28%)	2,403 (72%)
Primary Income	89 (20%)	365 (80%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Standard_criteria~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Standard_criteria~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.59 (1.33 to 1.92)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.37 (1.11 to 1.69)	0.003
Financial_role_
Other	—
Primary Income	1.07 (0.76 to 1.54)	0.69
Landlord_size_ * Financial_role_
big * Primary Income	1.56 (0.93 to 2.61)	0.091
¹ OR = Odds Ratio, CI = Confidence Interval

Offers to nonstandard

Outcome: how often offer tenancy to low-resource applicants who do not meet standard requirements

1: always or often
0: sometimes or rarely

Crosstabs:

df %>% 
  tbl_summary(
    by = Offers_to_nonstandard,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Offers to nonstandard**")

Characteristic	Offers to nonstandard		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	2,619 (92%)	236 (8.3%)
big	773 (88%)	105 (12%)
Financial_role_, n (%)			<0.001
Other	2,998 (92%)	269 (8.2%)
Primary Income	379 (84%)	71 (16%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Offers_to_nonstandard~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Offers_to_nonstandard~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.51 (1.18 to 1.92)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.38 (1.02 to 1.85)	0.035
Financial_role_
Other	—
Primary Income	2.41 (1.54 to 3.66)	<0.001
Landlord_size_ * Financial_role_
big * Primary Income	0.64 (0.35 to 1.17)	0.14
¹ OR = Odds Ratio, CI = Confidence Interval

Rent to vouchers

Outomce: ever rented to tenant using vouchers

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Rent_to_vouchers,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Rent to vouchers**")

Characteristic	Rent to vouchers		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	2,650 (90%)	296 (10%)
big	443 (50%)	447 (50%)
Financial_role_, n (%)			<0.001
Other	2,823 (84%)	538 (16%)
Primary Income	259 (57%)	198 (43%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Rent_to_vouchers~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Rent_to_vouchers~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	9.03 (7.57 to 10.8)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	8.12 (6.62 to 9.97)	<0.001
Financial_role_
Other	—
Primary Income	1.88 (1.21 to 2.84)	0.004
Landlord_size_ * Financial_role_
big * Primary Income	0.89 (0.54 to 1.51)	0.67
¹ OR = Odds Ratio, CI = Confidence Interval

Rent to criminal

Outcome: ever rented to tenant you knew had criminal record

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Rent_to_criminal,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Rents to criminal hx**")

Characteristic	Rents to criminal hx		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	2,650 (90%)	296 (10%)
big	570 (64%)	320 (36%)
Financial_role_, n (%)			<0.001
Other	2,923 (87%)	438 (13%)
Primary Income	286 (63%)	171 (37%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Rent_to_criminal~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Rent_to_criminal~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	5.03 (4.19 to 6.03)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	4.13 (3.32 to 5.12)	<0.001
Financial_role_
Other	—
Primary Income	2.43 (1.61 to 3.57)	<0.001
Landlord_size_ * Financial_role_
big * Primary Income	0.87 (0.54 to 1.44)	0.59
¹ OR = Odds Ratio, CI = Confidence Interval

Disability accomodations

Outcome: ever had tenant request disability accomodations

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Disability_accom.,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Disability accommodations**")

Characteristic	Disability accommodations		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	2,802 (95%)	144 (4.9%)
big	654 (73%)	236 (27%)
Financial_role_, n (%)			<0.001
Other	3,098 (92%)	263 (7.8%)
Primary Income	343 (75%)	114 (25%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Disability_accom.~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Disability_accom.~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	7.02 (5.62 to 8.80)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	6.40 (4.93 to 8.32)	<0.001
Financial_role_
Other	—
Primary Income	2.68 (1.56 to 4.36)	<0.001
Landlord_size_ * Financial_role_
big * Primary Income	0.61 (0.34 to 1.13)	0.10
¹ OR = Odds Ratio, CI = Confidence Interval

Fees

Deposit pct. kept

Outcome: what percentage of security/pet deposits did you retain

0: 0%
25: less than 25%
50: about 50%
75: more than 75%
100: 100%

Crosstabs:

df %>% 
  tbl_summary(
    by = Deposit_pct_kept,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Deposit pct kept**")

Characteristic	Deposit pct kept					p-value¹
Characteristic	0	25	50	75	100	p-value¹
Landlord_size_, n (%)						<0.001
small	1,415 (59%)	569 (24%)	108 (4.5%)	64 (2.7%)	245 (10%)
big	384 (45%)	292 (34%)	69 (8.1%)	21 (2.5%)	86 (10%)
Financial_role_, n (%)						<0.001
Other	1,611 (57%)	699 (25%)	138 (4.9%)	71 (2.5%)	293 (10%)
Primary Income	185 (43%)	157 (37%)	36 (8.4%)	14 (3.3%)	35 (8.2%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Deposit_pct_kept~1 + Landlord_size_,
          family=gaussian(link="identity"), data=df)

m2 <- glm(Deposit_pct_kept~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=gaussian(link="identity"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	exp(Beta) (95% CI)¹	p-value
Landlord_size_
small	—
big	65.6 (5.52 to 778)	<0.001
¹ CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	exp(Beta) (95% CI)¹	p-value
Landlord_size_
small	—
big	26.4 (1.44 to 485)	0.027
Financial_role_
Other	—
Primary Income	0.78 (0.00 to 140)	0.92
Landlord_size_ * Financial_role_
big * Primary Income	18.8 (0.02 to 18,730)	0.41
¹ CI = Confidence Interval

Deposit kept reason

Outcome: why did you retain some or all of deposit

0: cover damages
1: cover unpaid rent or fees

Crosstabs:

df %>% 
  tbl_summary(
    by = Deposit_kept_reason,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Deposit kept reason**")

Characteristic	Deposit kept reason		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.008
small	695 (80%)	174 (20%)
big	279 (73%)	102 (27%)
Financial_role_, n (%)			0.049
Other	819 (79%)	220 (21%)
Primary Income	148 (73%)	56 (27%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Deposit_kept_reason~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Deposit_kept_reason~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.46 (1.10 to 1.93)	0.008
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.31 (0.93 to 1.84)	0.12
Financial_role_
Other	—
Primary Income	1.01 (0.52 to 1.85)	0.97
Landlord_size_ * Financial_role_
big * Primary Income	1.33 (0.62 to 2.98)	0.48
¹ OR = Odds Ratio, CI = Confidence Interval

Allow installments

Outcome: prior to 2017, allow fees or deposits in installments

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Allow_installments,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Allow installments**")

Characteristic	Allow installments		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	1,612 (58%)	1,169 (42%)
big	323 (37%)	542 (63%)
Financial_role_, n (%)			<0.001
Other	1,742 (55%)	1,446 (45%)
Primary Income	186 (42%)	257 (58%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Allow_installments~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Allow_installments~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	2.31 (1.98 to 2.71)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	2.67 (2.21 to 3.23)	<0.001
Financial_role_
Other	—
Primary Income	2.06 (1.50 to 2.85)	<0.001
Landlord_size_ * Financial_role_
big * Primary Income	0.35 (0.23 to 0.55)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

Utilities included

Outcome: do you include utilities in rent for most common unit size

0: no
1: yes (any)

Crosstabs:

df %>% 
  tbl_summary(
    by = Utilities_included,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
 modify_spanning_header(starts_with("stat_") ~ "**Utilities included**")

Characteristic	Utilities included		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	1,761 (65%)	939 (35%)
big	387 (44%)	483 (56%)
Financial_role_, n (%)			0.024
Other	1,900 (61%)	1,219 (39%)
Primary Income	241 (55%)	195 (45%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Utilities_included~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Utilities_included~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	2.34 (2.00 to 2.74)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	2.54 (2.11 to 3.05)	<0.001
Financial_role_
Other	—
Primary Income	0.93 (0.66 to 1.31)	0.70
Landlord_size_ * Financial_role_
big * Primary Income	0.82 (0.53 to 1.29)	0.39
¹ OR = Odds Ratio, CI = Confidence Interval

Security deposit

Outcome: average security deposit fee for most common unit size

0: below or equal to the median fee by most common unit size
1: above the median fee by most common unit size

Crosstabs:

df %>% 
  tbl_summary(
    by = Security_deposit,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
  modify_spanning_header(starts_with("stat_") ~ "**Security deposit**")

Characteristic	Security deposit		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	1,575 (62%)	969 (38%)
big	603 (71%)	247 (29%)
Financial_role_, n (%)			0.016
Other	1,866 (63%)	1,080 (37%)
Primary Income	300 (69%)	133 (31%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Security_deposit~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Security_deposit~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.67 (0.56 to 0.79)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.73 (0.60 to 0.89)	0.002
Financial_role_
Other	—
Primary Income	1.13 (0.81 to 1.57)	0.47
Landlord_size_ * Financial_role_
big * Primary Income	0.66 (0.42 to 1.06)	0.083
¹ OR = Odds Ratio, CI = Confidence Interval

App fee

Outcome: average application fee for most common unit size

0: below or equal to the median fee by most common unit size
1: above the median fee by most common unit size

Crosstabs:

df %>% 
  tbl_summary(
    by = App_fee,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**App fee**")

Characteristic	App fee		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	1,162 (65%)	616 (35%)
big	328 (48%)	356 (52%)
Financial_role_, n (%)			<0.001
Other	1,322 (63%)	790 (37%)
Primary Income	162 (48%)	176 (52%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(App_fee~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(App_fee~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	2.05 (1.71 to 2.45)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.71 (1.39 to 2.12)	<0.001
Financial_role_
Other	—
Primary Income	1.05 (0.69 to 1.58)	0.81
Landlord_size_ * Financial_role_
big * Primary Income	1.53 (0.91 to 2.59)	0.11
¹ OR = Odds Ratio, CI = Confidence Interval

Cleaning fee

Outcome: average cleaning fee for most common unit size

0: below or equal to the median fee by most common unit size
1: above the median fee by most common unit size

Crosstabs:

df %>% 
  tbl_summary(
    by = Cleaning_fee,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Cleaning fee**")

Characteristic	Cleaning fee		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.16
small	970 (74%)	333 (26%)
big	327 (78%)	93 (22%)
Financial_role_, n (%)			0.61
Other	1,139 (75%)	378 (25%)
Primary Income	152 (77%)	46 (23%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Cleaning_fee~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Cleaning_fee~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.83 (0.64 to 1.07)	0.16
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.82 (0.60 to 1.11)	0.20
Financial_role_
Other	—
Primary Income	0.95 (0.54 to 1.59)	0.85
Landlord_size_ * Financial_role_
big * Primary Income	1.07 (0.51 to 2.25)	0.86
¹ OR = Odds Ratio, CI = Confidence Interval

Parking fee

Outcome: average parking fee for most common unit size

0: below or equal to the median fee by most common unit size
1: above the median fee by most common unit size

Crosstabs:

df %>% 
  tbl_summary(
    by = Parking_fee,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Parking fee**")

Characteristic	Parking fee		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	311 (91%)	30 (8.8%)
big	158 (75%)	53 (25%)
Financial_role_, n (%)			<0.001
Other	396 (89%)	50 (11%)
Primary Income	69 (68%)	33 (32%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Parking_fee~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Parking_fee~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	3.48 (2.15 to 5.71)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	2.30 (1.26 to 4.19)	0.006
Financial_role_
Other	—
Primary Income	1.53 (0.35 to 4.81)	0.51
Landlord_size_ * Financial_role_
big * Primary Income	1.90 (0.51 to 9.44)	0.38
¹ OR = Odds Ratio, CI = Confidence Interval

Rent

Fair market price

Outcome: average monthly rent for most common unit size above or below fair market price

0: below or equal to fair market price
1: above fair market price
note: here are 2018 king county fair market prices
- 1br=$1,108
- 2br=$1,243
- 3br=$1,527
- 4br=$2,211
- 5br=$2,617

Crosstabs:

df %>% 
  tbl_summary(
    by = Fair_market_price,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Fair market price**")

Characteristic	Fair market price		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	1,829 (67%)	911 (33%)
big	707 (80%)	175 (20%)
Financial_role_, n (%)			0.50
Other	2,204 (70%)	954 (30%)
Primary Income	319 (71%)	128 (29%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Fair_market_price~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Fair_market_price~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.50 (0.41 to 0.60)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.43 (0.34 to 0.53)	<0.001
Financial_role_
Other	—
Primary Income	1.13 (0.80 to 1.57)	0.48
Landlord_size_ * Financial_role_
big * Primary Income	1.38 (0.85 to 2.23)	0.19
¹ OR = Odds Ratio, CI = Confidence Interval

Last month required

Outcome: require last month’s rent before move in for most common unit size

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Last_month_required,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Last month required**")

Characteristic	Last month required		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	1,519 (56%)	1,175 (44%)
big	693 (79%)	181 (21%)
Financial_role_, n (%)			<0.001
Other	1,866 (60%)	1,240 (40%)
Primary Income	334 (75%)	112 (25%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Last_month_required~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Last_month_required~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.34 (0.28 to 0.40)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.37 (0.30 to 0.45)	<0.001
Financial_role_
Other	—
Primary Income	0.82 (0.59 to 1.13)	0.22
Landlord_size_ * Financial_role_
big * Primary Income	0.89 (0.55 to 1.45)	0.65
¹ OR = Odds Ratio, CI = Confidence Interval

Raised rent

Outcome: raised rent in past year

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Raised_rent,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Raised rent**")

Characteristic	Raised rent		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	1,236 (43%)	1,666 (57%)
big	122 (14%)	755 (86%)
Financial_role_, n (%)			<0.001
Other	1,268 (38%)	2,042 (62%)
Primary Income	82 (18%)	369 (82%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Raised_rent~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Raised_rent~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	4.59 (3.75 to 5.66)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	3.64 (2.91 to 4.59)	<0.001
Financial_role_
Other	—
Primary Income	1.30 (0.95 to 1.81)	0.11
Landlord_size_ * Financial_role_
big * Primary Income	1.98 (1.11 to 3.63)	0.023
¹ OR = Odds Ratio, CI = Confidence Interval

Raised rent pct.

Outcome: raised rent by how much percent

0: have not raised rent
3: 1-5%
7: 6-10%
13: 11-15%
20: 16-25%
30: more than 25%

Crosstabs:

df %>% 
  tbl_summary(
    by = Raised_rent_pct,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Raised rent pct**")

Characteristic	Raised rent pct					p-value¹
Characteristic	3	7	13	20	30	p-value¹
Landlord_size_, n (%)						<0.001
small	871 (52%)	540 (32%)	108 (6.5%)	93 (5.6%)	54 (3.2%)
big	338 (45%)	317 (42%)	67 (8.9%)	22 (2.9%)	11 (1.5%)
Financial_role_, n (%)						0.19
Other	1,035 (51%)	704 (34%)	149 (7.3%)	97 (4.8%)	57 (2.8%)
Primary Income	167 (45%)	151 (41%)	25 (6.8%)	18 (4.9%)	8 (2.2%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Raised_rent_pct~1 + Landlord_size_,
          family=gaussian(link="identity"), data=df)

m2 <- glm(Raised_rent_pct~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=gaussian(link="identity"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	exp(Beta) (95% CI)¹	p-value
Landlord_size_
small	—
big	0.73 (0.45 to 1.20)	0.21
¹ CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	exp(Beta) (95% CI)¹	p-value
Landlord_size_
small	—
big	0.82 (0.46 to 1.46)	0.50
Financial_role_
Other	—
Primary Income	2.47 (0.80 to 7.62)	0.12
Landlord_size_ * Financial_role_
big * Primary Income	0.35 (0.08 to 1.44)	0.15
¹ CI = Confidence Interval

Raised rent - rising costs

Outcome: raised rent due to rising costs of taxes, repairs

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Raised_rent_rising_costs,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Raised rent rising costs**")

Characteristic	Raised rent rising costs		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.69
small	190 (12%)	1,364 (88%)
big	83 (12%)	630 (88%)
Financial_role_, n (%)			0.51
Other	233 (12%)	1,677 (88%)
Primary Income	38 (11%)	309 (89%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Raised_rent_rising_costs~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Raised_rent_rising_costs~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.06 (0.81 to 1.40)	0.69
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.18 (0.85 to 1.66)	0.33
Financial_role_
Other	—
Primary Income	1.92 (0.94 to 4.61)	0.10
Landlord_size_ * Financial_role_
big * Primary Income	0.44 (0.17 to 1.06)	0.083
¹ OR = Odds Ratio, CI = Confidence Interval

Raised rent - regulations

Outcome: raised rent due to regulations

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Raised_rent_regulations,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Raised rent regulations**")

Characteristic	Raised rent regulations		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.28
small	1,531 (99%)	23 (1.5%)
big	698 (98%)	15 (2.1%)
Financial_role_, n (%)			0.22
Other	1,876 (98%)	34 (1.8%)
Primary Income	344 (99%)	3 (0.9%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Raised_rent_regulations~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Raised_rent_regulations~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.43 (0.73 to 2.73)	0.29
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.98 (0.96 to 3.95)	0.055
Financial_role_
Other	—
Primary Income	1.39 (0.22 to 4.83)	0.66
Landlord_size_ * Financial_role_
big * Primary Income	0.10 (0.00 to 1.17)	0.073
¹ OR = Odds Ratio, CI = Confidence Interval

Raised rent - market

Outcome: raised rent due to housing market

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Raised_rent_market,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Raised rent market**")

Characteristic	Raised rent market		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.23
small	1,396 (90%)	158 (10%)
big	652 (91%)	61 (8.6%)
Financial_role_, n (%)			0.76
Other	1,724 (90%)	186 (9.7%)
Primary Income	315 (91%)	32 (9.2%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Raised_rent_market~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Raised_rent_market~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.83 (0.60 to 1.12)	0.23
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.66 (0.44 to 0.96)	0.036
Financial_role_
Other	—
Primary Income	0.45 (0.16 to 1.01)	0.084
Landlord_size_ * Financial_role_
big * Primary Income	3.55 (1.32 to 11.4)	0.019
¹ OR = Odds Ratio, CI = Confidence Interval

Terminations

Ever terminated

Outcome: in 2017, did you terminate tenancies

0: none
1: 1 or more

Crosstabs:

df %>% 
  tbl_summary(
    by = Ever_terminated,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Ever terminated**")

Characteristic	Ever terminated		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	2,705 (92%)	220 (7.5%)
big	648 (73%)	236 (27%)
Financial_role_, n (%)			<0.001
Other	2,999 (90%)	337 (10%)
Primary Income	342 (75%)	114 (25%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Ever_terminated~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Ever_terminated~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	4.48 (3.66 to 5.49)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	3.86 (3.03 to 4.90)	<0.001
Financial_role_
Other	—
Primary Income	1.61 (0.95 to 2.59)	0.060
Landlord_size_ * Financial_role_
big * Primary Income	1.01 (0.57 to 1.86)	0.97
¹ OR = Odds Ratio, CI = Confidence Interval

Num of terminations

Ouctome: in 2017, how many tenancies did you terminate

3: 1-5
7: 6-10
13: 11-20
25: more than 20

Crosstabs:

df %>% 
  tbl_summary(
    by = Num_of_terminations,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Number of terminations**")

Characteristic	Number of terminations				p-value¹
Characteristic	3	7	15	25	p-value¹
Landlord_size_, n (%)					0.11
small	185 (97%)	5 (2.6%)	0 (0%)	1 (0.5%)
big	204 (92%)	9 (4.1%)	5 (2.3%)	3 (1.4%)
Financial_role_, n (%)					0.002
Other	289 (96%)	7 (2.3%)	5 (1.7%)	0 (0%)
Primary Income	96 (91%)	6 (5.7%)	0 (0%)	4 (3.8%)
¹ Fisher's exact test

Models:

m1 <- glm(Num_of_terminations~1 + Landlord_size_,
          family=gaussian(link="identity"), data=df)

m2 <- glm(Num_of_terminations~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=gaussian(link="identity"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	exp(Beta) (95% CI)¹	p-value
Landlord_size_
small	—
big	1.67 (1.01 to 2.76)	0.046
¹ CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	exp(Beta) (95% CI)¹	p-value
Landlord_size_
small	—
big	1.69 (0.94 to 3.05)	0.083
Financial_role_
Other	—
Primary Income	4.31 (1.19 to 15.6)	0.027
Landlord_size_ * Financial_role_
big * Primary Income	0.34 (0.08 to 1.46)	0.15
¹ CI = Confidence Interval

Terminated - paid late

Outcome: any terminated due to late payment

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Terminated_paid_late,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Terminated - paid late**")

Characteristic	Terminated - paid late		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.009
small	13 (23%)	44 (77%)
big	4 (6.2%)	60 (94%)
Financial_role_, n (%)			0.35
Other	15 (16%)	77 (84%)
Primary Income	2 (7.1%)	26 (93%)
¹ Pearson's Chi-squared test; Fisher's exact test

Models:

m1 <- glm(Terminated_paid_late~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Terminated_paid_late~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	4.43 (1.46 to 16.6)	0.014
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	2.29 (0.71 to 8.85)	0.19
Financial_role_
Other	—
Primary Income	0.26 (0.03 to 2.38)	0.20
Landlord_size_ * Financial_role_
big * Primary Income	50,464,928 (0.00 to NA)	0.99
¹ OR = Odds Ratio, CI = Confidence Interval

Terminated - non payment

Outcome: any terminated due to late payment

0: no
1: yes

Crosstabs:

df %>% 
  tbl_summary(
    by = Terminated_non_payment,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Terminated - non payment**")

Characteristic	Terminated - non payment		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	13 (18%)	60 (82%)
big	4 (2.7%)	144 (97%)
Financial_role_, n (%)			0.037
Other	15 (11%)	127 (89%)
Primary Income	2 (2.6%)	74 (97%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Terminated_non_payment~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Terminated_non_payment~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	7.80 (2.64 to 28.6)	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	3.38 (1.09 to 12.7)	0.046
Financial_role_
Other	—
Primary Income	0.19 (0.02 to 1.74)	0.12
Landlord_size_ * Financial_role_
big * Primary Income	93,085,001 (0.00 to NA)	0.99
¹ OR = Odds Ratio, CI = Confidence Interval

Terminated - use courts

Ouctome: of the terminations, were these handled with the courts

0: always or usually handle with tenants
1: always or usually handle with the courts

Crosstabs:

df %>% 
  tbl_summary(
    by = Terminated_use_courts,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Terminated - use of courts**")

Characteristic	Terminated - use of courts		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			<0.001
small	22 (0.8%)	2,903 (99%)
big	66 (7.4%)	820 (93%)
Financial_role_, n (%)			<0.001
Other	52 (1.6%)	3,289 (98%)
Primary Income	35 (7.7%)	417 (92%)
¹ Pearson's Chi-squared test

Models:


m1 <- glm(Terminated_use_courts~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Terminated_use_courts~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)
tbl_regression(m2, exponentiate=TRUE)

Renter Profile, Future

Renter income estimate

Outcome: average income of renter by most common unit size (used midpoints)

13: less than 25,000
37: 25,000-50,000
62: 50,000-75,000
100: 75,000-150,000
200: more than 150,000

Crosstabs:

df %>% 
  tbl_summary(
    by = Renter_income_estimate,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Renter income estimate (unit=1000)**")

Characteristic	Renter income estimate (unit=1000)					p-value¹
Characteristic	13	37	62	100	200	p-value¹
Landlord_size_, n (%)						<0.001
small	124 (4.6%)	518 (19%)	755 (28%)	1,041 (38%)	276 (10%)
big	44 (5.4%)	296 (36%)	319 (39%)	149 (18%)	11 (1.3%)
Financial_role_, n (%)						<0.001
Other	130 (4.2%)	685 (22%)	919 (30%)	1,085 (35%)	274 (8.9%)
Primary Income	33 (7.8%)	127 (30%)	150 (35%)	100 (24%)	13 (3.1%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Renter_income_estimate~1 + Landlord_size_,
          family=gaussian(link="identity"), data=df)

m2 <- glm(Renter_income_estimate~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=gaussian(link="identity"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	exp(Beta) (95% CI)¹	p-value
Landlord_size_
small	—
big	0.00 (0.00 to 0.00)	<0.001
¹ CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	exp(Beta) (95% CI)¹	p-value
Landlord_size_
small	—
big	0.00 (0.00 to 0.00)	<0.001
Financial_role_
Other	—
Primary Income	0.00 (0.00 to 0.00)	<0.001
Landlord_size_ * Financial_role_
big * Primary Income	1,298,911 (92.2 to 18,295,749,646)	0.004
¹ CI = Confidence Interval

Still be prop mngr in 1, 5 yr

Outcome: do you expect to remain a property manager

0: unsure, definitely or propably not in 1 or 5 yrs
1: definitely or probably in 1 or 5 yrs

Crosstabs:

df %>% 
  tbl_summary(
    by = Still_mngr_1_5yrs,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Still be manager in 1 or 5 yrs**")

Characteristic	Still be manager in 1 or 5 yrs		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.24
small	2,369 (81%)	554 (19%)
big	731 (83%)	152 (17%)
Financial_role_, n (%)			0.87
Other	2,714 (81%)	618 (19%)
Primary Income	370 (81%)	86 (19%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Still_mngr_1_5yrs~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Still_mngr_1_5yrs~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.89 (0.73 to 1.08)	0.24
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	0.97 (0.77 to 1.22)	0.81
Financial_role_
Other	—
Primary Income	1.40 (0.96 to 2.00)	0.071
Landlord_size_ * Financial_role_
big * Primary Income	0.60 (0.35 to 1.01)	0.055
¹ OR = Odds Ratio, CI = Confidence Interval

Stop being prop mngr burdensome regs

Outcome: why might you stop being property manager

0: other reasons
1: burdensome regulatory environment

Crosstabs:

df %>% 
  tbl_summary(
    by = Stop_mngr_burdensome_regs,
    percent = "row",
    missing = "no",
    include = c(Landlord_size_, Financial_role_),
    statistic = list(all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels() %>%
  modify_header(update = all_stat_cols() ~ "**{level}**") %>% # Remove the Ns from the header row
modify_spanning_header(starts_with("stat_") ~ "**Stop managing due to regulations**")

Characteristic	Stop managing due to regulations		p-value¹
Characteristic	0	1	p-value¹
Landlord_size_, n (%)			0.69
small	2,525 (86%)	421 (14%)
big	758 (85%)	132 (15%)
Financial_role_, n (%)			0.16
Other	2,885 (86%)	476 (14%)
Primary Income	381 (83%)	76 (17%)
¹ Pearson's Chi-squared test

Models:

m1 <- glm(Stop_mngr_burdensome_regs~1 + Landlord_size_,
          family=binomial(link="logit"), data=df)

m2 <- glm(Stop_mngr_burdensome_regs~1 + Landlord_size_ +
            Financial_role_ + (Landlord_size_ * Financial_role_),
          family=binomial(link="logit"), data=df)



tbl_regression(m1, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.04 (0.84 to 1.29)	0.69
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_regression(m2, exponentiate=TRUE)

Characteristic	OR (95% CI)¹	p-value
Landlord_size_
small	—
big	1.19 (0.93 to 1.52)	0.16
Financial_role_
Other	—
Primary Income	1.88 (1.28 to 2.70)	<0.001
Landlord_size_ * Financial_role_
big * Primary Income	0.41 (0.24 to 0.72)	0.002
¹ OR = Odds Ratio, CI = Confidence Interval

SRHS Simple Models

Courtney Allen

27 September, 2021

Business practices, landlord size, and financial role

Leasing

Advertising

Screening

Flexible decision

Standard criteria

Offers to nonstandard

Rent to vouchers

Rent to criminal

Disability accomodations

Fees

Deposit pct. kept

Deposit kept reason

Allow installments

Utilities included

Security deposit

App fee

Cleaning fee

Parking fee

Rent

Fair market price

Last month required

Raised rent

Raised rent pct.

Raised rent - rising costs

Raised rent - regulations

Raised rent - market

Terminations

Ever terminated

Num of terminations

Terminated - paid late

Terminated - non payment

Terminated - use courts

Renter Profile, Future

Renter income estimate

Still be prop mngr in 1, 5 yr

Stop being prop mngr burdensome regs