This website provides interactive mapping of outstanding residential mortgage lending by postcode sector across Great Britain, as published by individual banks, via the Council of Mortgage Lenders. This first iteration of the website - published in January 2014 - uses the most recent bank lending data, which covers the period up to the end of June 2013. I hope to update the website with future data releases, if I have the time. The map is coloured so that there are roughly the same number of areas in each category displayed in the key to the right. It's important to remember that this data release covers only seven major lenders and about three quarters of the mortgage market - it is not the full story but it does give us interesting insights that were previously not possible. The release did not include mortgage lending data for Northern Ireland, so that's why it's not included here. I've included a large interactive map on the home page and if you click below that you can see a full screen map. I've also added in some tabs which show postcode sectors in and around London, Glasgow, Manchester and Cardiff but if you want to find somewhere else you can easily pan and zoom to it via the big map.

MAPPING INEQUALITY Redlining in New Deal America Atlanta How Owners' Loan Corporation 1938 Mapping Inequality introduces viewer to the records of the Home Owners' Loan Corporation on a scale that is unprecedented. Here you can browse more than 150 interactive maps and thousands of "area descriptions." These materials afford an extraordinary view of the contours of wealth and racial inequality in Depression-era American cities and insights into discriminatory policies and practices that so profoundly shaped cities that we feel their legacy to this day.https://dsl.richmond.edu/panorama/redlining/

The Public Use Database (PUDB) is released annually to meet FHFA’s requirement under 12 U.S.C. 4543 and 4546(d) to publicly disclose data about the Enterprises’ single-family and multifamily mortgage acquisitions. The datasets supply mortgage lenders, planners, researchers, policymakers, and housing advocates with information concerning the flow of mortgage credit in America’s neighborhoods. Beginning with data for mortgages acquired in 2018, FHFA has ordered that the PUDB be expanded to include additional data that is the same as the data definitions used by the regulations implementing the Home Mortgage Disclosure Act, as required by 12 U.S.C. 4543(a)(2) and 4546(d)(1).The PUDB single-family datasets include loan-level records that include data elements on the income, race, and sex of each borrower as well as the census tract location of the property, loan-to-value (LTV) ratio, age of mortgage note, and affordability of the mortgage. New for 2018 are the inclusion of the borrower’s debt-to-income (DTI) ratio and detailed LTV ratio data at the census tract level. The PUDB multifamily property-level datasets include information on the unpaid principal balance and type of seller/servicer from which the Enterprise acquired the mortgage. New for 2018 is the inclusion of property size data at the census tract level. The multifamily unit-class files also include information on the number and affordability of the units in the property. Both the single-family and multifamily datasets include indicators of whether the purchases are from “underserved” census tracts, as defined in terms of median income and minority percentage of population.Prior to 2010 the single-family PUDB consisted of three files: Census Tract, National A, and National B files. With the 2010 PUDB a fourth file, National C, was added to provide information on high-cost mortgages acquired by the Enterprises. The single-family Census Tract file includes information on the location of the property based on the 2010 Census for acquisition years 2012 through 2021, and the 2020 Census beginning with the 2022 acquisition year. The National files contain other information but lack detailed geographic information in order to protect Enterprise proprietary data. The multifamily datasets also consist of a Census Tract file, and a National file without detailed geographic information.Several dashboards are available to analyze the data:Enterprise Multifamily Public Use Database DashboardThe Enterprise Multifamily Public Use Database (PUDB) Dashboard provides users an interactive way to generate and visualize Enterprise PUDB data of multifamily mortgage acquisitions by Fannie Mae and Freddie Mac. It shows characteristics about multifamily loans, properties and units at the national level, and characteristics about multifamily loans and properties at the state level. It includes key statistics, time series charts, and state maps of multifamily housing characteristics such as median loan amount, number of properties, average number of units per property, and unit affordability. The underlying aggregate statistics presented in the dashboard come from three multifamily data files in the Enterprise PUDB, updated annually since 2008, including two property-level datasets and a data file on the size and affordability of individual units.Enterprise Multifamily Public Use DashboardPress Release - FHFA Releases Data Visualization Dashboard for Enterprises’ Multifamily Mortgage AcquisitionsMortgage Loan and Natural Disaster DashboardFHFA published an interactive Mortgage Loan and Natural Disaster Dashboard that combines FHFA’s PUDB reports on single-family and multifamily acquisitions for the regulated entities, FEMA’s National Risk Index (NRI), and FHFA’s Duty to Serve 2023 High-Needs rural areas. Desired geographies can be exported to .pdf and Excel from the Public Use Database and National Risk Index Dashboard.Mortgage Loan and Natural Disaster DashboardMortgage Loan and Natural Disaster Dashboard FAQs

This web map shows the Hong Kong Domestic Households Distribution with Mortgage Payment or Loan Repayment by Monthly Domestic Household Mortgage Payment and Loan Repayment by Small TPU in 2021. It is a subset of the 2021 Population Census made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is in CSV format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of CSDI Portal at https://portal.csdi.gov.hk.

This layer shows Hong Kong Domestic households distribution with mortgage payment or loan repayment by monthly domestic household mortgage payment and loan repayment by large TPU in 2016. It is a subset of the census data 2016 made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in XLSX format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

This layer shows the Hong Kong Domestic Households Distribution with Mortgage Payment or Loan Repayment by Monthly Domestic Household Mortgage Payment and Loan Repayment by Large Tertiary Planning Unit Group in 2021. It is a subset of the 2021 Population Census made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is in CSV format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of CSDI Portal at https://portal.csdi.gov.hk.

The practice of redlining was codified by a series of maps created as part of the New Deal by the Home Owners’ Loan Corporation, which evaluated the mortgage lending risk of neighborhoods.

This layer shows Hong Kong Domestic households distribution with mortgage payment or loan repayment by monthly domestic household mortgage payment and loan repayment by small TPU in 2016. It is a subset of the census data 2016 made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in XLSX format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

Graph and download economic data for Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS) from Q1 1991 to Q1 2025 about credit cards, delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

Graph and download economic data for Delinquency Rate on Consumer Loans, All Commercial Banks (DRCLACBS) from Q1 1987 to Q1 2025 about delinquencies, commercial, loans, consumer, banks, depository institutions, rate, and USA.

The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red.This layer is an extract of the ArcGIS Online nationwide layer, clipped to Los Angeles County.For more information about this dataset, please contact egis@isd.lacounty.gov

Macroeconomic research finds that expansionary and contractionary monetary policies may have asymmetric effects, yet microeconomic research on the relationship between deposits and loans implicitly or explicitly assumes that relationship is symmetric. This research challenges that assumption and explores potential asymmetries in how bank deposit changes translate into lending. Using Jayaratne and Morgan’s (2000) analytical approach with panel data on bank financials from 2003 to 2017, we consider both point-in-time asymmetries, and asymmetries that vary over time.

Real enumeration district (ED) overlap with virtual enumeration districts.

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-07-02 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

Abstract

Scaling-up of performance-based financing (PBF) schemes across sub-saharan Africa has developed rapidly over the past few years. Many studies have shown a positive association between PBF and health service coverage, and some with improvements in quality. However, a lack of controls and confounders in most studies that have been published on PBF initiatives means that the impact of PBF initiatives on service coverage, quality and health outcomes remains open to question. Moreover, few studies have examined the factors that influence the impact of PBF- an area of considerable operational significance since PBF often involves a package of constituent interventions: linking payment and results, independent verification of results, managerial autonomy to facilities and enhanced systematic supervision of facilities. As a result, the policy objectives of the following Impact Evaluation are to: (a) identify the impact of PBF on Maternal and Child Health (MCH) service coverage and quality; (b) identify key factors responsible for this impact; and (c) assess cost-effectiveness of PBF as a strategy to improve coverage and quality. The results from the impact evaluation will be useful to designing national PBF policy in Cameroon and will also contribute to the larger body of knowledge on Performance-based Financing (PBF).

The impact evaluation is a blocked-by-region cluster-randomized trial (CRT), having a pre-post with comparison design. The evaluation relies primarily on experimental control to answer the main research questions for this study. Individual health facilities in each region have been randomized to one of the 4 study groups. Individual public and private primary care health facilities in 14 districts from the 3 pilot regions have been randomly assigned to each study group to create a factorial study design.

The evaluation relied on two main sources of data: - Household surveys: A household survey was implemented at baseline (i.e., before implementation of PBF begins), and at endline (i.e., after PBF has been implemented for two years). - Facility-based surveys: A facility-based survey was implemented at baseline and at endline.

Geographic coverage

14 health districts across North-West (4), South-West (4) and East (6) regions.

Analysis unit

Public and private health facilities (providing primary and/or secondary care)

Kind of data

Sample survey data [ssd]

Sampling procedure

The facility survey will be conducted at baseline and endline in all public CMAs, CSIs and District Hospitals in the 14 districts included in the impact evaluation and a sample of private facilities in these districts. Based on a health facility mapping exercise conducted prior to the baseline survey, there was a total of 242 primary care facilities and 20 secondary care facilities (district and private hospitals) in the 14 districts included in the impact evaluation. Primary care and secondary care facilities combined, this included 81 in the East, 91 in the North-West and 88 in the South-West for a total of 262. Out of these, 40 were private for profit facilities. As private for-profit facilities were added to the sample after the signature of the contract with IFORD (baseline survey firm), it was decided that a random sample of 20 primary care private for-profit facilities and all private hospitals would be taken, due to budget constraints. Thus the target number of facilities was 222 primary care facilities and 20 secondary care facilities (district hospitals and private hospitals). All facility team visits will be unannounced. The facility-based survey includes multiple components, described below.

Sampling deviation

The original expected sample: - based on a minimum of 5 respondents for each module in each sampled facility - was in fact unrealistic given (i) the realities of the demand and supply of health services in the study districts and the (ii) data collection plan and budgeting. Due to budget constraints, each health facility was only visited for one day during unannounced visits. Thus the survey teams were limited to the number of patients and providers that were present on the day of the survey.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Facility-based survey The facility survey was conducted in all the CMAs, CSIs and District Hospitals in the 14 districts included in the impact evaluation. All facility team visits were unannounced. The facility-based survey included multiple components. The sample of health workers, patient-provider observations and client exit interviews was selected to enable findings from these three components to be linked.

Facility assessment module The facility assessment module collected data on key aspects of facility functioning and structural aspects of quality of care. The individual in charge of the health facility at the time when the survey team visited the health facility was asked to be the respondent for this survey module. The main themes that were covered by the facility assessment included:

• Facility staffing, including the staffing complement of the facility, staff on duty at the time of the survey team’s visit and staff present at the time of the survey team’s visit • Facility infrastructure and equipment • Availability of drugs, consumables and supplies at the health facility • Supervision • Record keeping and reporting to the Health Management Information System • Facility management • Official user charges at the facility • Revenues obtained at the health facility, and how revenues have been used

Health worker interview module For health facilities with more than five health workers, a list of all clinical staff who worked in the area of maternal and child health providing prenatal or under five consultations was obtained. If this list contained more than five people, study enumerators interviewed a random sample of these health workers. If the list contained fewer than five people, all clinical personnel working in maternal and child health were interviewed. The interviews focused on the following areas:

• Role and responsibilities of the interviewed health worker • Compensation, including delays in salary payments • Staff satisfaction and motivation

Observations of patient-provider interaction module While the health worker interview module collects information on what health workers know, the purpose of this module is to gather information on what health workers actually do with their patients.

A member of the survey team observed consultations with a systematic random sample of patients under five presenting with a new condition (i.e., not for follow-up visits or routine) and new ANC clients. The observer used a structured format to note whether key desired actions were carried out. In the case of patients under five, the instruments were focused on whether IMCI protocols are followed. For ANC clients the instruments examined whether key desired actions (including counseling) were carried out. As primary care facilities do not offer ANC services on all days of the week – typically these are offered 2 days each week – the ANC module was not conducted at all health facilities. During the baseline survey, 5 under-5 and 5 ANC observations were conducted at each facility where these modules are implemented. After finding that many health facilities did not offer ANC on the day of the survey at baseline, during the endline survey enumerators were asked to interview as many women receiving ANC on the day of the survey as possible to increase the sample size. All health workers selected for patient-provider observations will be included in the health worker interview sample.

Patient exit interviews Enumerators conducted an exit interview with all patients whose consultation was observed as part of the study procedures. If the patient was a child, the child’s caregiver was interviewed. The under-fives included in the patient exit sample were the same children whose consultation with a provider was observed. In addition to this, exit interviews were conducted with all ANC clients whose consultation with a provider was observed.

HOLC Frequency in real and virtual enumeration districts.

Abstract

Scaling-up of performance-based financing (PBF) schemes across sub-saharan Africa has developed rapidly over the past few years. Many studies have shown a positive association between PBF and health service coverage, and some with improvements in quality. However, a lack of controls and confounders in most studies that have been published on PBF initiatives means that the impact of PBF initiatives on service coverage, quality and health outcomes remains open to question. Moreover, few studies have examined the factors that influence the impact of PBF- an area of considerable operational significance since PBF often involves a package of constituent interventions: linking payment and results, independent verification of results, managerial autonomy to facilities and enhanced systematic supervision of facilities. As a result, the policy objectives of the following Impact Evaluation are to: (a) identify the impact of PBF on Maternal and Child Health (MCH) service coverage and quality; (b) identify key factors responsible for this impact; and (c) assess cost-effectiveness of PBF as a strategy to improve coverage and quality. The results from the impact evaluation will be useful to designing national PBF policy in Cameroon and will also contribute to the larger body of knowledge on Performance-based Financing (PBF).

The impact evaluation is a blocked-by-region cluster-randomized trial (CRT), having a pre-post with comparison design. The evaluation relies primarily on experimental control to answer the main research questions for this study. Individual health facilities in each region have been randomized to one of the 4 study groups. Individual public and private primary care health facilities in 14 districts from the 3 pilot regions have been randomly assigned to each study group to create a factorial study design.

The evaluation relied on two main sources of data: - Household surveys: A household survey was implemented at baseline (i.e., before implementation of PBF begins), and at endline (i.e., after PBF has been implemented for two years). - Facility-based surveys: A facility-based survey was implemented at baseline and at endline.

Note: The Household Baseline Survey is available online under Impact Evaluation Surveys Collection. The study is titled "Health Results-Based Financing Impact Evaluation 2012, Household Baseline Survey."

Geographic coverage

Littoral, North-West, South-West and East regions of Cameroon.

Analysis unit

Public and private health facilities (providing primary and/or secondary care).

Kind of data

Sample survey data [ssd]

Sampling procedure

The facility survey will be conducted at baseline and endline in all public CMAs, CSIs and District Hospitals in the 14 districts included in the impact evaluation and a sample of private facilities in these districts. Based on a health facility mapping exercise conducted prior to the baseline survey, there was a total of 242 primary care facilities and 20 secondary care facilities (district and private hospitals) in the 14 districts included in the impact evaluation. Primary care and secondary care facilities combined, this included 81 in the East, 91 in the North-West and 88 in the South-West for a total of 262. Out of these, 40 were private for profit facilities. As private for-profit facilities were added to the sample after the signature of the contract with IFORD (baseline survey firm), it was decided that a random sample of 20 primary care private for-profit facilities and all private hospitals would be taken, due to budget constraints. Thus the target number of facilities was 222 primary care facilities and 20 secondary care facilities (district hospitals and private hospitals). All facility team visits will be unannounced. The facility-based survey includes multiple components, described below.

Sampling deviation

The original expected sample - based on a minimum of 5 respondents for each module in each sampled facility- was in fact unrealistic given (i) the realities of the demand and supply of health services in the study districts and the (ii) data collection plan and budgeting. Due to budget constraints, each health facility was only visited for one day during unannounced visits. Thus the survey teams were limited to the number of patients and providers that were present on the day of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Components of the health facility baseline survey included the following surveys:

• Facility assessment module (F1): The facility assessment module seeks to collect data on key aspects of facility functioning and structural aspects of quality of care. The respondent for this module are individuals in charge of the health facility at the time when the survey team visits the health facility.

• Health worker interview module (F2): A stratified random sample of clinical health workers with maternal and child health service delivery responsibilities at sampled health facilities was interviewed as part of this module.

• Observations of patient-provider interaction module (F3 and F4): The purpose of this module is to gather information on what health workers actually do with their patients.

• Patient exit interviews (F5, F6 and F7): A systematic random sample of patients visiting the facility (an expected 5 patients aged under-five and 5 patients aged over 5) for curative care with a new complaint will be interviewed to assess the patient's perception of quality of care and satisfaction at all 245 primary care facilities surveyed. If the patient is a child, the child's caregiver will be interviewed. The 5 under-fives included in the patient exit sample will be the same 5 children whose consultation with a provider was observed. In addition to this, exit interviews will be conducted with all ANC clients whose consultation with a provider was observed.

Response rate

Overall, 93.8% of targeted facilities were surveyed. The remaining 6% were either inaccessible or not functional (closed down) at the time of the survey.

Abstract

Zambia was awarded a grant in 2008 by the World Bank through the Health Results Innovation Trust Fund (HRITF) to implement a RBF pilot project with an accompanying Impact Evaluation (IE) led by the World Bank. Motivated by inadequate progress to achieving MDGs 4 and 5 targets, the primary objective of the project was to catalyze the country’s efforts to reduce under-five and maternal mortality in 11 districts in nine (9) of Zambia’s 10 provinces (except Lusaka) countrywide.

The Zambia health RBF (HRBF) pilot project was implemented by the Government through the Zambian health system (contracted-in) and is one of the few examples of a Lower Middle Income Country (LMIC) with this type of model. After a pre-pilot phase, which lasted approximately 2 years in the Eastern Province district of Katete, the RBF model was expanded to ten (10) additional districts in April 2012. By the end of the project, 203 health centres were covered across the country. This represented a total catchment population of about 1.5 million people of which the direct beneficiaries were 338,248 children aged between 0-59 months, and 372,073 women of childbearing age.

The accompanying IE comprised both quantitative and qualitative approaches. Quantitative data for the IE at household and facility level was collected at baseline, implementation stage, and endline from 10 RBF intervention districts; 10 Control 1 (C1) districts; and 10 Control 2 (C2) districts. The method of selecting districts for the IE was based on district-matched randomization. Inputs were assigned to the three district groups as follows: (a) The RBF Intervention group to receive Emergency Obstetric and Neonatal Care (EmONC) equipment and RBF performance-based grants; (b) The C1 group (“enhanced financing” arm) to receive EmONC equipment exactly as in the RBF and the equivalent in money of the average RBF performance-related grants as input financing; and (c) The C2 (“pure control” arm) group to receive nothing.

The IE investigated the impact of the RBF over a broad range of targeted and non-targeted indicators related to maternal and child health services. Baseline household data was collected over the period November to December 2011. Endline data was collected between November 2014 and January 2015, using the same survey tools and in the same study areas and was undertaken in 18 IE districts (all of the study districts in six of the matched district triplets yielding information from 6 RBF districts, 6 C1 districts, and 6 C2 districts). For the health facility survey, baseline data was collected between November and December 2011, and endline data was collected between November 2014 and January 2015.

Note: The household, health facility baseline and community data and are available online as separate entries under Impact Evaluation Surveys Collection in the Central Data Catalog.

Geographic coverage

Survey was undertaken in 30 districts (all of the study districts of the 10 matched district triplets across 8 provinces: Central, Copperbelt, Eastern, Luapula, Northern, North-Western, Southern, Western).

Analysis unit

Health Facilities

Sampling procedure

Within each province (except for Northern and Southern Provinces, where six districts were sampled), three districts at or near a derived provincial median index score were selected and then randomly assigned to each of the three arms. Thus, there are a total of 30 districts distributed equally among the three study arms with 10 districts in each.

For statistical purposes, each district in Zambia is subdivided into Census Supervisory Areas (CSAs), which in turn nests Standard Enumeration Areas (SEAs). Thus, for data collection purposes, the SEA is the smallest geographical unit above the household and is the primary sampling unit (PSU). The SEAs were sampled from the catchment areas of selected health facilities.

The sampling frame of SEAs in each treatment arm was arrived at by digitally overlaying SEA maps (obtained from the CSO) with health facility catchment area maps. After grouping the PSUs by stratum (treatment vs control), the sample was then selected in two stages: i) selection of PSUs in the first stage using probability proportional to size, and ii) selection of 10 eligible households, or secondary sampling units (SSUs), in the second stage using systematic random sampling. Prior to household selection, a full PSU listing of eligible households (households with a pregnancy related outcome, i.e. live birth, stillbirth, abortion and miscarriage within the two years prior to the survey) was undertaken by the survey team in each cluster. At baseline, 3,064 households in the relevant districts were surveyed at baseline, and 3,500 households at follow up. Health facilities were selected by a simple random sampling technique. In the baseline, 176 health facilities were surveyed whereas 210 were surveyed in the endline.

Mode of data collection

Other [oth]

This dataset provides values for INTEREST RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This web map shows the location and contact information of self financing post-secondary institutions in Hong Kong. It is a set of data made available by the Education Bureau under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.