Housing affordability is a major concern for many Los Angeles County residents. Housing constitutes the single largest monthly expense for most people. Among homeowners, their homes are often their largest financial assets. Home ownership can also offer many benefits, including the opportunity to increase financial security and build wealth.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Suppose there is an investment fund planning to invest in properties at hundreds of locations. 🏠 Based on the previous millions of property sales over the past few years, the fund house wants to identify the property which can result in a higher gain on investment. 💰 They can not go by analyzing all the properties one by one. 🤔 So they want the segmentation of properties so that they can look into their target segments. 🎯 So this challenge is going to help them by easily identifying their target properties using advanced AI and Analytics. 🔍

In the first week, you will receive a dataset of real estate properties with locality, estimated price, and selling price for the last 23 years. 📊 The task is to identify the input features in the dataset and use them to predict the sale price of a property. 🔮 After this modelling of input and output features, predict the sale price of all the properties in the test dataset. 💵 Once the sale prices for the test data are predicted, put these properties into 4 segments. 🔢 These segments can be formed according to the gain. 💹 The gain is calculated based on the estimated price and predicted sale price (Gain = (Sale price - Estimated price)/100).

Finally, you need to submit your results as the segment level for each of the properties given in the test data. 📝 For reference, the properties need to be segmented into the following 4 segments according to the gain calculated based on the predicted sale:-

0: Premium Properties 💰🏰 1: Valuable Properties 💎🏡 2: Standard Properties 🏘️💸 3: Budget Properties 🏠💵

This database by department provides mainly data on localised social and tax income and personalised autonomy allowance. It contains nearly 100 variables. This database makes it possible to study the standard of living as well as the inequalities of poverty of this population. It will also make it possible to observe the ageing population by counting the number of people using allowances and their amount, in order to allow them to stay in their homes or to help them pay part of the Ephad in which they reside. Data determining the degree of loss of autonomy of these individuals (divided into 6 groups called “GIR”) ranging from a person still independent to one person at the end of life, it will be possible to study and compare the number of these people per department and to see if some departments are more affected by the ageing of the population than others. This database also contains indicators projected at 2050 on the number of people, their average age and their life expectancy per high or low group. Census data will also show the type of housing for people aged 60 and over.

Housing affordability is a major concern for many Los Angeles County residents. Housing constitutes the single largest monthly expense for most people. Among homeowners, their homes are often their largest financial assets. Home ownership can also offer many benefits, including the opportunity to increase financial security and build wealth.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The Housing and Economic Recovery Act of 2008 (HERA) requires the Federal Housing Finance Agency (FHFA) to submit an annual report to Congress on the collateral pledged to the FHLBanks, including an analysis of collateral by type and by Bank district.3 FHFA’s Report on Collateral Pledged to Federal Home Loan Banks provides the required information as well as additional analysis of data on the types and amounts of collateral pledged to the Banks to secure advances and other collateralized products offered by the Banks to their members. The information in this report uses data collected through a quarterly data collection conducted by FHFA’s Division of Federal Home Loan Bank Regulation (DBR).

HMDA requires many Financial Institutions (FI)s to maintain, report, and publicly disclose information about applications for and originations of mortgage loans. HMDA s purposes are to provide the public and public officials with sufficient information to enable them to determine whether institutions are serving the housing needs of the communities and neighborhoods in which they are located, to assist public officials in distributing public sector investments in a manner designed to improve the private investment environment, and to assist in identifying possible discriminatory lending patterns and enforcing antidiscrimination statutes. This publicly-available data asset contains HMDA data collected in or after 2017 and has been modified to protect the privacy of individuals whose information is present in the dataset.

This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each spending unit (usually the husband, the main earner, or the owner of the home) was interviewed. The basic unit of reference in the study was the spending unit, but some family data are also available. The questions in the 1961 survey covered the respondent's attitudes toward national economic conditions and price activity, as well as the respondent's own financial situation. Other questions examined the spending unit head's occupation, and the nature and amount of the spending unit's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of cars and other major durables. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. The survey also gathered detailed information on jobs and job histories. Personal data include number of people in the spending unit, age, sex, and education of the head, and the race and sex of the respondent. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07441.v2. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Knowing who your consumers are is essential for businesses, marketers, and researchers. This detailed demographic file offers an in-depth look at American consumers, packed with insights about personal details, household information, financial status, and lifestyle choices. Let's take a closer look at the data:

Personal Identifiers and Basic Demographics At the heart of this dataset are the key details that make up a consumer profile:

Unique IDs (PID, HHID) for individuals and households Full names (First, Middle, Last) and suffixes Gender and age Date of birth Complete location details (address, city, state, ZIP) These identifiers are critical for accurate marketing and form the base for deeper analysis.

Geospatial Intelligence This file goes beyond just listing addresses by including rich geospatial data like:

Latitude and longitude Census tract and block details Codes for Metropolitan Statistical Areas (MSA) and Core-Based Statistical Areas (CBSA) County size codes Geocoding accuracy This allows for precise geographic segmentation and localized marketing.

Housing and Property Data The dataset covers a lot of ground when it comes to housing, providing valuable insights for real estate professionals, lenders, and home service providers:

Homeownership status Dwelling type (single-family, multi-family, etc.) Property values (market, assessed, and appraised) Year built and square footage Room count, amenities like fireplaces or pools, and building quality This data is crucial for targeting homeowners with products and services like refinancing or home improvement offers.

Wealth and Financial Data For a deeper dive into consumer wealth, the file includes:

Estimated household income Wealth scores Credit card usage Mortgage info (loan amounts, rates, terms) Home equity estimates and investment property ownership These indicators are invaluable for financial services, luxury brands, and fundraising organizations looking to reach affluent individuals.

Lifestyle and Interests One of the most useful features of the dataset is its extensive lifestyle segmentation:

Hobbies and interests (e.g., gardening, travel, sports) Book preferences, magazine subscriptions Outdoor activities (camping, fishing, hunting) Pet ownership, tech usage, political views, and religious affiliations This data is perfect for crafting personalized marketing campaigns and developing products that align with specific consumer preferences.

Consumer Behavior and Purchase Habits The file also sheds light on how consumers behave and shop:

Online and catalog shopping preferences Gift-giving tendencies, presence of children, vehicle ownership Media consumption (TV, radio, internet) Retailers and e-commerce businesses will find this behavioral data especially useful for tailoring their outreach.

Demographic Clusters and Segmentation Pre-built segments like:

Household, neighborhood, family, and digital clusters Generational and lifestage groups make it easier to quickly target specific demographics, streamlining the process for market analysis and campaign planning.

Ethnicity and Language Preferences In today's multicultural market, knowing your audience's cultural background is key. The file includes:

Ethnicity codes and language preferences Flags for Hispanic/Spanish-speaking households This helps ensure culturally relevant and sensitive communication.

Education and Occupation Data The dataset also tracks education and career info:

Education level and occupation codes Home-based business indicators This data is essential for B2B marketers, recruitment agencies, and education-focused campaigns.

Digital and Social Media Habits With everyone online, digital behavior insights are a must:

Internet, TV, radio, and magazine usage Social media platform engagement (Facebook, Instagram, LinkedIn) Streaming subscriptions (Netflix, Hulu) This data helps marketers, app developers, and social media managers connect with their audience in the digital space.

Political and Charitable Tendencies For political campaigns or non-profits, this dataset offers:

Political affiliations and outlook Charitable donation history Volunteer activities These insights are perfect for cause-related marketing and targeted political outreach.

Neighborhood Characteristics By incorporating census data, the file provides a bigger picture of the consumer's environment:

Population density, racial composition, and age distribution Housing occupancy and ownership rates This offers important context for understanding the demographic landscape.

Predictive Consumer Indexes The dataset includes forward-looking indicators in categories like:

Fashion, automotive, and beauty products Health, home decor, pet products, sports, and travel These predictive insights help businesses anticipate consumer trends and needs.

Contact Information Finally, the file includes ke...

This dataset is a summary of the OpenFEMA Individuals and Households Program - Valid Registrations (NEMIS) dataset and contains aggregated, non-PII data from Housing Assistance Program reporting authority within FEMA's Recovery Directorate to share data on registrations and Individuals and Households Program (IHP). The data contains counts of program eligibility, referrals and registration methods as well as program award amounts segmented by city where registration is valid. Additionally disaster number, county and zip code are provided.rnrnPlease Note: IHP is intended to help with critical expenses that cannot be covered in other ways. The IHP is not intended to return all homes or belongings to their pre†disaster condition. In some cases, IHP may only provide enough money, up to the program limits, for you to return an item to service. Secondary or vacation residencies do not qualify. Visit for more information about the program: https://www.fema.gov/assistance/public . rnrnThis is raw, unedited data from FEMA's National Emergency Management Information System (NEMIS) and as such is subject to a small percentage of human error. rnrnThe financial information is derived from NEMIS and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and how business rules are applied, this financial information may differ slightly from official publication on public websites such as usaspending.gov; this dataset is not intended to be used for any official federal financial reporting.rnrnCitation: The Agency's preferred citation for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions .rnrnThis dataset is not intended to be an official federal report, and should not be considered an official federal report.rnrnIf you have media inquiries about this dataset, please email the FEMA News Desk FEMA-News-Desk@dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open government program please contact the OpenFEMA team via email OpenFEMA@fema.dhs.gov.

Analysis of ‘Department of Housing & Community Development Performance Metrics FY 2011-2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4899bbff-6087-4694-9682-f48ec6a1dc81 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Maryland D​epartment of Housing and Community Development is proud to be at the forefront in implementing housing policy that promotes and preserves homeownership and creating innovative community development initiatives to meet the challenges of a growing Maryland.

Through the Maryland Mortgage Program, the department has empowered thousands of Maryland families to realize the American dream of homeownership and for existing homeowners.

The department’s rental housing programs increase and preserve the supply of affordable housing and provide good choices for working families, senior citizens, and individuals with special needs.

Community development and revitalization programs like Neighborhood BusinessWorks, Community Legacy, and Main Street Maryland help our cities and towns remain rich, vibrant communities.

The Maryland Department of Housing and Community Development remains committed to building on our past successes to maintain our reputation as an innovator in community revitalization and a national leader in housing finance.

DISCLAIMER: Some of the information may be tied to the Department’s bond funded loan programs and should not be relied upon in making an investment decision. The Department provides comprehensive quarterly and annual financial information and operating data regarding its bonds and bond funded loan programs, all of which is posted on the publicly-accessible Electronic Municipal Market Access system website (commonly known as EMMA) that is maintained by the Municipal Securities Rulemaking Board, and on the Department’s website under Investor Information.

More information accessible here: http://dhcd.maryland.gov/Investors/Pages/default.aspx

--- Original source retains full ownership of the source dataset ---

SAFI (Studying African Farmer-Led Irrigation) is a currently running project which is looking at farming and irrigation methods. This is survey data relating to households and agriculture in Tanzania and Mozambique. The survey data was collected through interviews conducted between November 2016 and June 2017. The survey covered such things as; household features (e.g. construction materials used, number of household members), agricultural practices (e.g. water usage), assets (e.g. number and types of livestock) and details about the household members.This is a teaching version of the collected data, it is not the full dataset. The survey is split into several sections:A – General questions about when and where the survey was conducted.B - Information about the household and how long they have been living in the areaC – Details about the accommodation and other buildings on the farmD – Details about the different plots of land they grow crops onE – Details about how they irrigate the land and availability of waterF – Financial details including assets owned and sources of incomeG – Details of Financial hardshipsX – Information collected directly from the smartphone (GPS) or automatically included in the form (instanceID)key_id Added to provide a unique Id for each observation. (The InstanceID field does this as well but it is not as convenient to use)A01_interview_date, Date of InterviewA03_quest_no, Questionnaire numberA04_start, Timestamp of start of InterviewA05_end, Timestamp of end of InterviewA06_province, Province nameA07_district, District nameA08_ward, Ward nameA09_village, Village nameA11_years_farm, Number of years the household have been farming in this areaA12_agr_assoc, Does the head of the household belong to an agricultural association_note2 Possible form comment relating to the sectionB_no_membrs, How many members of the household?_members_count Internal count of membersB11_remittance_money, Is there any financial assistance from family members not living on the farmB16_years_liv, How many years have you been living in this village or neighbouring village?B17_parents_liv, Did your parents live in this village or neighbouring village?B18_sp_parents_liv, Did your spouse's parents live in this village or neighbouring village?B19_grand_liv, Did your grandparents live in this village or neighbouring village?B20_sp_grand_liv, Did your spouse's grandparents live in this village or neighbouring village?C01_respondent_roof_type, What type of roof does their house have?C02_respondent_wall_type, What type of walls does their house have (from list)C02_respondent_wall_type_other, What type of walls does their house have (not on list)C03_respondent_floor_type, What type of floor does their house have C04_window_type, Does the house have glass in at least one window?C05_buildings_in_compound, How many buildings are in the compound? Do not include stores, toilets or temporary structures.C06_rooms, How many rooms in the main house are used for sleeping?C07_other_buildings, Does the DU own any other buildings other than those on this plotD_no_plots, How many plots were cultivated in the last 12 months?D_plots_count, Internal count of plotsE01_water_use, Do you bring water to your fields, stop water leaving your fields or drain water out of any of your fields?E_no_group_count, How many plots are irrigated?E_yes_group_count, How many plots are not irrigated?E17_no_enough_water, Are there months when you cannot get enough water for your crops? Indicate which months.E18_months_no_water, Please select the monthsE19_period_use, For how long have you been using these methods of watering crops? (years)E20_exper_other, Do you have experience of such methods on other farms?E21_other_meth, Have you used other methods before?E22_res_change, Why did you change the way of watering your crops?E23_memb_assoc, Are you a member of an irrigation association?E24_resp_assoc, Do you have responsibilities in that association?E25_fees_water, Do you pay fees to use water?E26_affect_conflicts, Have you been affected by conflicts with other irrigators in the area ?_note Form comment for sectionF04_need_money, If you started or changed the way you water your crops recently, did you need any money for it?F05_money_source, Where did the money came from? (list)F05_money_source_other, Where did the money came from? (not on list)F06_crops_contr, Considering fields where you have applied water, how much do those crops contribute to your overall income?F08_emply_lab, In the most recent cultivation season, did you employ day labourers on fields?F09_du_labour, In the most recent cultivation season, did anyone in the household undertake day labour work on other farm?F10_liv_owned, What types of livestock do you own? (list)F10_liv_owned_other, What types of livestock do you own? (not on list)F_liv_count, Livestock countF12_poultry, Own poultry?F13_du_look_aftr_cows, At the present time, does the household look after cows for someone else in return for milk or money?F14_items_owned, Which of the following items are owned by the household? (list)F14_items_owned_other, Which of the following items are owned by the household? (not on list)G01_no_meals, How many meals do people in your household normally eat in a day?G02_months_lack_food, Indicate which months, In the last 12 months have you faced a situation when you did not have enough food to feed the household?G03_no_food_mitigation, When you have faced such a situation what do you do?gps:Latitude, Location latitude (provided by smartphone)gps:Longitude, Location Longitude (provided by smartphone)gps:Altitude, Location Altitude (provided by smartphone)gps:Accuracy, Location accuracy (provided by smartphone)instanceID, Unique identifier for the form data submission

The Maryland Department of Housing and Community Development offers multifamily finance programs for the construction and rehabilitation of affordable rental housing units for low to moderate income families, senior citizens and individuals with disabilities. Our multifamily bond programs issues tax-exempt and taxable revenue mortgage bonds to finance the acquisition, preservation and creation of affordable multifamily rental housing units in priority funding areas. By advocating for increased production of rental housing units, we help create much-needed jobs and leverage opportunities to live, work and prosper for hardworking Maryland families, senior citizens, and individuals with disabilities throughout the state.​ DISCLAIMER: Some of the information may be tied to the Department’s bond funded loan programs and should not be relied upon in making an investment decision. The Department provides comprehensive quarterly and annual financial information and operating data regarding its bonds and bond funded loan programs, all of which is posted on the publicly-accessible Electronic Municipal Market Access system website (commonly known as EMMA) that is maintained by the Municipal Securities Rulemaking Board, and on the Department’s website under Investor Information. More information accessible here: http://dhcd.maryland.gov/Investors/Pages/default.aspx

This dataset provides information about people applying for loans, including details on their personal background, finances, and loan specifics. It's meant to help us better understand how different personal factors impact whether a loan gets approved. The data includes things like the applicant's age, income, home ownership status, job history, and credit score, along with loan details such as the loan amount, interest rate, and purpose. It also shows whether the loan was approved or denied.

Features in the dataset:

1. person_age: The applicant's age.
2. person_income: How much the applicant earns annually, in USD.
3. person_home_ownership: Whether the person owns, rents, or has a mortgage on their home.
4. person_emp_length: How long the person has been employed.
5. loan_intent: The reason for applying for the loan (e.g., EDUCATION, MEDICAL, VENTURE, DEBT CONSOLIDATION, PERSONAL).
6. loan_grade: The credit grade given to the loan (A, B, C, D, etc.).
7. loan_amnt: The amount of money the applicant is requesting for the loan, in USD.
8. loan_int_rate: The interest rate on the loan.
9. loan_percent_income: The percentage of the applicant’s income being requested as a loan.
10. cb_person_default_on_file: Whether the applicant has defaulted on any previous loans (Y/N).
11. cb_person_cred_hist_length: The length of the applicant's credit history, in years.
12. loan_status: The outcome of the loan application (0 = Rejected, 1 = Accepted).

​ ​​​​FHFA is required to monitor and report annually on the Federal Home Loan Banks' support of their low-income housing and community development activities to the Federal Home Loan Banks' Advisory Councils. This report fulfills that requirement. This report addresses the FHLBanks’ activities to support low-income housing and community development. The FHLBanks support a range of these activities through three programs: the statutorily-mandated Affordable Housing Program (AHP), the statutorily-mandated Community Investment Program (CIP), and the voluntary Community Investment Cash Advance Program (CICA). Under these programs, the FHLBanks provide loans (referred to as advances) and grants to their members, and their members then use these funds to assist very low- and low- or moderate-income households and communities. The report also covers FHLBank Community Support Programs, non-depository Community Development Financial Institution (CDFI) membership, and FHLBank performance on housing goals. ​

Analysis of ‘* Beneficiaries receiving housing assistance — per municipality ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/560d9161a3a7294a8e94aa17 on 17 January 2022.

--- Dataset description provided by original source is as follows ---

_A from 2016 onwards the December duty month data are observed with a decrease of 6 months (final data) instead of 2 months (semi-definitive data). _ This dataset is an enumeration, per municipality, of persons who are members of the beneficiary households who are entitled to housing assistance, for December of the reference year. It is a means-tested benefit. Housing allowances are intended to support small people and families in their financial effort for the main housing. They concern tenants, household residents and owner-occupiers. Housing subsidies consist of the Family Housing Allowance (ALF), the Social Housing Allowance (ALS) and the Personal Housing Support (APL). More detailed information on housing subsidies can be found on the caf.fr site at the following link: Http://www.caf.fr/allocataires/droits-et-prestations/s-informer-sur-les-aides/logement-et-cadre-de-vie/les-aides-au-logement?active=tab1 The geographical field of observation of the dataset corresponds to the municipality of residence of the beneficiary household as recorded in the statistical file of beneficiaries for the month of entitlement, irrespective of which Caf managed the beneficiary file. For more information, please read me.

--- Original source retains full ownership of the source dataset ---

Abstract copyright UK Data Service and data collection copyright owner.

The English Housing Survey (EHS ) Fuel Poverty Datasets are comprised of fuel poverty variables derived from the EHS, and a number of EHS variables commonly used in fuel poverty reporting. The EHS is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England.

End User Licence and Special Licence Versions
Similar to the main EHS, two versions of the Fuel Poverty dataset are available from 2014 onwards. The Special Licence version contains additional, more detailed, variables, and is therefore subject to more restrictive access conditions. Users should check the End User Licence version first to see whether it meeds their needs, before making an application for the Special Licence version.

Fuel Poverty Dataset

The fuel poverty dataset is comprised of fuel poverty variables derived from the English Housing Survey (EHS), and a number of EHS variables commonly used in fuel poverty reporting. The fieldwork for the EHS is carried out each financial year (between April and March). The fuel poverty datasets combine data from two consecutive financial years. Full information on the EHS survey is available at the MHCLG EHS website and further information on Fuel Poverty and the EHS can be sought from FuelPoverty@beis.gov.uk and ehs@communities.gov.uk respectively. Guidance on use of EHS data provided by MHCLG should also be applied to the fuel poverty dataset.

Further information may be found in the Annual Fuel Poverty Statistics Report: 2020 (2018 Data) on the gov.uk website.

Latest edition information

For the second edition (June 2021) the data file was replaced with a new version, with some errors corrected in the labelling of numeric values.

Main Topics:

The data cover modelled household fuel costs and consumption. See documentation for further details.

Analysis of ‘Financial Services for NYCHA Residents by Borough- Local Law 163 - CY2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f7e8f8a1-a08b-4aa0-b2a7-9d111bfbb3be on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains information for Calendar Year 2020 about NYCHA residents’ use of: a) NYC Financial Empowerment Centers: a program that provides free, one-on-one professional financial counseling and coaching to all NYC residents. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; b) EmpoweredNYC: is an initiative to assist New Yorkers with disabilities and their families to better manage their finances and become more financially stable. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; c) Ready to Rent: a program providing free one-on-one financial counseling to New Yorkers seeking to apply for affordable housing units through HPD’s Housing Connect lottery. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; and d) TLC Owner/Driver Resource Center: a program that provides free financial counseling and legal assistance for TLC Licensees, and connects for-hire drivers with other resources. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service

The dataset is part of the annual report compiled by the Mayor’s Office of Operations as mandated by the Local Law 163 of 2016 on different services provided to NYCHA residents. See other datasets in this report by searching the keyword “Services available to NYCHA Residents - Local Law 163 (2016)” on the Open Data Portal. This dataset is not intended to be used to determine personally identifying information, determine socioeconomic status, or identify racial or ethnic groups of NYCHA residents that receive this service. The dataset is aggregated by a geographical unit to protect the privacy of individuals. Some columns have "N/A" values and/or other intricacies, and their explanations are provided under the “Notes” tab in Data Dictionary. Client’s outcome data are collected in the DCWP client database while NYCHA residency is tracked using Building Identification Number from client reported address matching a NYCHA Development.

--- Original source retains full ownership of the source dataset ---

The EVIDENT serious game explores consumer behaviour in response to a malfunctioning home appliance. Specifically, it examines how consumers approach decisions to repair or replace a broken home appliance and the impact of behavioural biases on these decisions. There are two key aims addressed within the EVIDENT serious game. 1) Determine the impact of socio-demographic factors, environmental literacy, and financial literacy on consumer willingness to pay for the repair of home appliances. 2) Determine the impact of information and education mediated through a serious game on consumer in-game and real-world repair/replace decision-making.

The serious game itself is a life-simulation game in which users are tasked with maintaining their virtual home while ensuring their avatar remains comfortable (i.e. basic needs such as hunger, warmth and hygiene are met) while monitoring their financial and energy consumption. Within this game, users learn that an appliance has malfunctioned, and a repairperson is called. Users must then determine how best to proceed by entering a negotiation with the repairperson.

The experiment consists of the following sections: 1) demographic information; 2) financial literacy; 3) environmental literacy; 4) serious game. The game receives as input the replies of the participant on the demographics information section to provide a personalized gameplay experience. Replies regarding participant's age ("What is your age?"), role ("Which of the following apply to you?"), income ("What is your household's annual income?"), gender ("Which character would you like to play with?") and family status ("How many people live in your home (including you) - Children") will be used to adjust players' avatar, starting amount of money, size of the house, age of the player and the negotiation process with the repair person.

The negotiation process differs based on the participants' role ("Which of the following apply to you?"). In this question, the participant can choose one of the following replies: 1) I am a homeowner, 2) I am a tenant (i.e. I pay someone to rent my accommodation), 3) I am a landlord (i.e. I receive payment for accommodation from someone else). Participants who rent (2) or are landlords (3) will be assigned to an additional in-game scenario to explore the unique context in which their energy decisions are made. Random allocation to a role will be applied for participants who select multiple options (i.e., homeowners who are also landlords).

More information on the EVIDENT Serious Game Experiment can be found on the public deliverables of the EVIDENT project https://evident-h2020.eu/deliverables/. More specifically, the serious game implementation design is described in deliverable D2.3 Serious game implementation design, the design of the experiment is reported in D2.2 Optimised Protocols Design, and the experiment preparatory actions are described in D3.1 Specifications of preparatory actions for RCT, surveys and serious game and D3.2 Implementation of preparatory actions for RCT, surveys and serious game.

Finally, the EVIDENT serious game can be found in the following locations:

EVIDENT Website: https://evident-h2020.eu/seriousgame

Google Play: https://play.google.com/store/apps/details?id=com.CERTH.EvidentSeriousGame

App Store: https://apps.apple.com/gr/app/evident-serious-game/id6447255106

EVIDENT Platform (participation in the experiment): https://platform.evident-h2020.eu/sessions/participate_session/1560d6e6-732a-470c-807a-c70472d51c53

This dataset is an enumeration of persons who are members of the recipient households living in Sailly-Les-Lannoy and have a paymentable entitlement to housing assistance, for December of the reference year. This is a benefit paid under conditions of resources. Housing allowances are intended to support modest individuals and families in their financial effort devoted to the main housing. They concern tenants, residents in homes and accessors to the property. Housing aids consist of the Family Housing Allowance (ALF), the Social Housing Allowance (ALS), and the Personalised Housing Assistance (APL). The geographical field of observation of the dataset corresponds to the municipality of residence of the recipient household as recorded in the statistical file of recipients of the month of entitlement, regardless of the Caf which managed the recipient file. Source: CAF, http://data.caf.fr/dataset/population-des-foyers-allocatair