Taiwan area land office jurisdictional data.......

This table shows the average House Price/Earnings ratio, which is an important indicator of housing affordability. Ratios are calculated by dividing house price by the median earnings of a borough. The Annual Survey of Hours and Earnings (ASHE) is based on a 1 per cent sample of employee jobs. Information on earnings and hours is obtained in confidence from employers. It does not cover the self-employed nor does it cover employees not paid during the reference period. Information is as at April each year. The statistics used are workplace based full-time individual earnings. Pre-2013 Land Registry housing data are for the first half of the year only, so that they are comparable to the ASHE data which are as at April. This is no longer the case from 2013 onwards as this data uses house price data from the ONS House Price Statistics for Small Areas statistical release. Prior to 2006 data are not available for Inner and Outer London. The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile. The "lower quartile" property price/income is determined by ranking all property prices/incomes in ascending order. The 'median' property price/income is determined by ranking all property prices/incomes in ascending order. The point at which one half of the values are above and one half are below is the median. Regional data has not been published by DCLG since 2012. Data for regions has been calculated by the GLA. Data since 2014 has been calculated by the GLA using Land Registry house prices and ONS Earnings data. Link to DCLG Live Tables An interactive map showing the affordability ratios by local authority for 2013, 2014 and 2015 is also available.

🇬🇧 United Kingdom English This table shows the average House Price/Earnings ratio, which is an important indicator of housing affordability. Ratios are calculated by dividing house price by the median earnings of a borough. The Annual Survey of Hours and Earnings (ASHE) is based on a 1 per cent sample of employee jobs. Information on earnings and hours is obtained in confidence from employers. It does not cover the self-employed nor does it cover employees not paid during the reference period. Information is as at April each year. The statistics used are workplace based full-time individual earnings. Pre-2013 Land Registry housing data are for the first half of the year only, so that they are comparable to the ASHE data which are as at April. This is no longer the case from 2013 onwards as this data uses house price data from the ONS House Price Statistics for Small Areas statistical release. Prior to 2006 data are not available for Inner and Outer London. The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile. The "lower quartile" property price/income is determined by ranking all property prices/incomes in ascending order. The 'median' property price/income is determined by ranking all property prices/incomes in ascending order. The point at which one half of the values are above and one half are below is the median. Regional data has not been published by DCLG since 2012. Data for regions has been calculated by the GLA. Data since 2014 has been calculated by the GLA using Land Registry house prices and ONS Earnings data. Link to DCLG Live Tables An interactive map showing the affordability ratios by local authority for 2013, 2014 and 2015 is also available.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

Output formatted extracts from ALKIS, property map with land parcel certificate Rhineland-Palatinate (RP33), output forms: analogue or print-prepared (PDF). In the property register, data of a factual and legal nature must be provided on all properties (lots and buildings), including data on the owners and hereditary builders of the plots. The property register consists in particular of the property map and the property description. The property map is the scaled down and leveled graphical representation of all in the official property cadastre information system (ALKIS®) managed properties. The presentation of the property map on a scale of 1: 1 000 is an official property map and a graphical basis for the official list of plots of land within the meaning of Paragraph 2(2) of the Land Registry Code. The product RP33 - Property map with parcel identification is a combination product of RP31 and RP21 for a parcel. The product RP21 - Land parcel certificate contains all land parcel information as well as public-law determinations including soil estimation, evaluation and information on the land parcel. The product RP31 - Property map shows the graphic representation of the plot on the selected scale (free scale). The choice of scale 1:1000 (official property map) may be necessary due to specific requirements of external persons and bodies.

Output formatted extracts from ALKIS, property map with further information Rhineland-Palatinate (RP32), output forms: analogue or print-prepared (PDF). In the property register, data of a factual and legal nature must be provided on all properties (lots and buildings), including data on the owners and hereditary builders of the plots. The property register consists in particular of the property map and the property description. The property map is the scaled down and leveled graphical representation of all in the official property cadastre information system (ALKIS®) managed properties. The presentation of the property map on a scale of 1: 1 000 is an official property map and a graphical basis for the official list of plots of land within the meaning of Paragraph 2(2) of the Land Registry Code. The product RP32 - Property map with further information corresponds to the product RP31 plus the soil estimation data and shows the graphical representation of the plot on the selected scale (free scale). The choice of scale 1:1000 (official property map) may be necessary due to specific requirements of external persons and bodies.

Explore land values and property sales information from across NSW Please see this guide on how to use the NSW land values and property sales map: User Guide Search land values Access information including: land values for the past five years (where available) the valuing year used to calculate council rates the valuation basis the property number, address, and zoning information the area and boundaries of non strata properties notice of any concessions or allowances that apply to the land value. The map does not show land values for individual strata properties. Find property sales Access property sales information including: property sales information for individual properties from 2001 property sales information at a street and suburb level for the last five years (where available area for non strata properties the dealing number and sale date (or contract date) the date the property sales information was last updated whether the property is strata or non strata, or if the sale is part of a multi property sale. We only include property sales information where the purchase price is $100 or over. NSW Land Registry Services provides property sales information to the Valuer General. This usually occurs within eight weeks of the settlement of property transfer. Contact us Phone : 1800 110 038 Mon-Fri, 8:30am – 5:00pm Via our Contact Us formFind an interpreter Please call TIS National on 131 450 and ask them to call Valuation Services on 1800 110 038. Metadata Content Title NSW land value and property sales web map Content Type Web Application Description All datasets except NSW land values and property sales information in this web maps are maintained by Spatial Service. Property NSW provides Land value and property Sales information. Update frequency for each dataset varies depending on the dataset. All these datasets are used in the land values and property sales map web map application. Please see individual metadata for each dataset below. For more information regarding the Land valuation and Property Sales information data please contact : valuationenquiry@property.nsw.gov.au For all other datasets, please contact ss-sds@customerservice.nsw.gov.au Initial Publication Date 21/12/2021 Data Currency 21/12/2021 Data Update Frequency Other Content Source File Type Web Feature Service Attribution Data Theme, Classification or Relationship to other Datasets Accuracy Spatial Reference System (dataset) WGS84 Spatial Reference System (web service) EPSG:4326 WGS84 Equivalent To GDA94 Spatial Extent Content Lineage Data Classification Unclassified Data Access Policy Open Data Quality Terms and Conditions Creative Commons Standard and Specification Data Custodian NSW Spatial Services Point of Contact NSW Spatial Services Data Aggregator Data Distributor Additional Supporting Information TRIM Number

This is an all-purpose viewer application for the Cleveland property survey 2022 results. It offers a lookup tool, various heat maps, and reporting by criteria that the user can choose.InstructionsViewer pageThe main view for looking up and searching property surveys. The heatmap is fixed to show clusters of D and F properties to guide the user's eyes to areas to explore further.Heatmaps pageExplore different clusters of the grades in this view. Switching back to Viewer will pan the map to the same place.Charts pageSee summary statistics about a given selection of property surveys, starting by default with all surveys. Use filters on the left to narrow down your interest and understand relationships between variables.Data GlossaryFor more information about the dataset, see the City-version of 2022 WRLC Property Survey layerThis app uses the following dataset(s):Citywide Property Survey 2022ContactsDro Sohrabian, Urban Analytics & Innovation

Access to SOLUS mapsDescriptionSOLUS100AccessData Citations DescriptionSoil Landscapes of the United States, or SOLUS, is a national map product developed by the National Cooperative Soil Survey that is focused on providing a consistent set of spatially continuous soil property maps to support large scope soil investigations and land use decisions. SOLUS maps use a digital soil mapping framework that combines multiple sources of soil survey data with environmental covariate data and machine learning. Digital soil mapping is the production of georeferenced soil databases based on the quantitative relationships between soil measurements made in the field or laboratory and environmental data. Numerical models use the quantitative relationships to predict the spatial distribution of either discrete soil classes, such as map units, or continuous soil properties, such as clay content. SOLUS maps use continuous property mapping, which predicts soil physical or chemical properties in horizontal and vertical dimensions. The soil properties are represented across a continuous range of values. Raster datasets of select soil properties can be predicted at specified depths or depth intervals. Continuous soil property maps such as SOLUS provide critical natural resource information to support environmental researchers and modelers, conservationists, and others making land management decisions. SOLUS will be updated annually with improved data and methodology. SOLUS100The first version of SOLUS, called SOLUS100, is 100 m spatial resolution. Each 100 m raster cell represents a 100 m by 100 m square on the ground with soil property values estimated at seven depths: 0, 5, 15, 30, 60, 100, and 150 cm. The next version will be 30 m spatial resolution and called SOLUS30. SOLUS100 predicts 20 soil properties (listed below with units) at seven depths for the continental United States for a total of 512 maps.Very fine sand (%)Fine sand (%)Medium sand (%)Coarse sand (%)Very coarse sand (%)Total sand (%)Silt (%)Clay (%)pHSoil organic carbon (%)Calcium carbonate equivalent (%)Gypsum content (% by weight)Electrical conductivity (mmhos/cm)Sodium adsorption ratioCation exchange capacity (meq/100g)Effective cation exchange capacity (meq/100g)Oven dry bulk density (g/cm3)Depth to bedrock (cm)Depth to restriction (cm)Rock fragment volume (%)Property Prediction and Uncertainty LayersEach property-depth prediction is accompanied by estimates of uncertainty expressed as prediction interval low and high and relative prediction interval (RPI). Prediction interval low and high define the range within which future predictions may occur. The relative prediction interval ranges from 0 to 1 and is a relative measure of uncertainty with high values being more uncertain. It is computed as the ratio of the 95% prediction interval width to the training set 95% quantile width (97.5% quantile value – 2.5% quantile value). Values closer to 0 indicate lower uncertainty and values closer to 1 indicate higher uncertainty. Values greater than 1 indicate that the prediction at that location is outside the range of the training data used for that property at that depth. The Soil and Plant Science Division delivers each property-depth combination through Google Cloud Platform as four raster data layers: the property prediction, the prediction interval low and high, and the RPI. Property prediction and uncertainty layers follow the naming convention: propertyname_depth_cm_p (predicted property values)propertyname_depth_cm_rpi (relative prediction interval)propertyname_depth_cm_l (prediction interval low)propertyname_depth_cm_h (prediction interval high)SOLUS100 map of clay content predicted at the 0 cm depth for the continental U.S.AccessSOLUS100 maps are available for download or use within scripting or GIS software environments: SOLUS100 Cloud Storage BucketDetails on background, methodology, accuracy, uncertainty, and other results and discussion of SOLUS100 maps are available at SOLUS100 Ag Data Commons Repository and in the following publication:Nauman, T. W., Kienast-Brown, S., Roecker, S. M., Brungard, C., White, D., Philippe, J., & Thompson, J. A. (2024). Soil landscapes of the United States (SOLUS): developing predictive soil property maps of the conterminous United States using hybrid training sets. Soil Science Society of America Journal, 1–20. https://doi.org/10.1002/saj2.20769Data CitationsSoil Survey Staff. Soil Landscapes of the United States. United States Department of Agriculture, Natural Resources Conservation Service. Available online at storage.googleapis.com/solus100pub/index.html. Month, day, year accessed (year of official release).Citation ExampleThe following example is for the 2024 SOLUS maps. Such citations should appear in the reference section of your document.Soil Survey Staff. Soil Landscapes of the United States. United States Department of Agriculture, Natural Resources Conservation Service. Available online at storage.googleapis.com/solus100pub/index.html. May 22, 2024 (2024 official release).

Output formatted extracts from ALKIS, property map with Orthophoto Rhineland-Palatinate (RPOF) analogue or print-prepared (PDF). In the property register, data of a factual and legal nature must be provided on all properties (lots and buildings), including data on the owners and hereditary builders of the plots. The property register consists in particular of the property map and the property description. The property map is the scaled down and leveled graphical representation of all properties listed in the Official Property Cadastral Information System (ALKIS®). The presentation of the property map on a scale of 1: 1 000 is an official property map and a graphical basis for the official list of plots of land within the meaning of Paragraph 2(2) of the Land Registry Code. The product RPOF shows the graphic representation of the parcel on the selected scale (free scale) together with the associated orthophoto. This allows for optimal orientation. The choice of scale 1:1000 (official property map) may be necessary due to specific requirements of external persons and bodies.

• land \tvalues for the past five years (where available) \t

  \t
• the \tvaluing year used to calculate council rates \t \t
• the \tvaluation basis \t \t
• the \tproperty number, address, and zoning information \t \t
• the area \tand boundaries of non strata properties \t \t
• notice of \tany concessions or allowances that apply to the land value. \t

The map does not show land values for individual strata properties.

• property \tsales information for individual properties from 2001 \t \t
• property \tsales information at a street and suburb level for the last five \tyears (where available \t
• area for \tnon strata properties \t \t
• the \tdealing number and sale date (or contract date) \t \t
• the date \tthe property sales information was last updated \t \t
• whether \tthe property is strata or non strata, or if the sale is part of a \tmulti property sale. \t

Contact us

Phone : 1800 110 038

Mon-Fri, 8:30am – 5:00pm

Please call TIS National on 131 450 and ask them to call Valuation Services on 1800 110 038.

Metadata

This series consists of records of the Chief Engineer's copy of the Melbourne and Metropolitan Tramways Board Register of Estates transferred and related documents. They provide a record of transfer of land ownership, titles, encumbrances, property particulars (including locality, municipality, purchaser and improvements) and plans.

* The First Register is separated into alphabetical sections with pages (from A1 to W3); and Section V is in the Second Register.
- Running Index and Cross Index
- Copy of Title - with Certificate of Title, site plan, encumbrances
- Particulars of Property - Locality, Municipality, Purchased From, Date Purchased, Purchaser, Cost and Utilised for; Locality Plan, Valuation - Year, Capital [Land, Building] Municipal; Improvements Added During Year and Remarks
- Plans of Land - Including P number, FB reference and Floor plans of Buildings compiled from Architect's Plans
- Copy of Conveyance for road widening
- Copy of Lease
- Loose paper notes

Property plans include Depot, Substations, Engine Houses, Easements, Stables, Car Sheds, Repair Shops, Mess Rooms, Overhead Crossings, Vacant Land, Workshops, Storage Depot, Parks, Road Widenings, Shunt, Residences, Embankments and Drainage.

*The Third Register is an unbound (loose paper), earlier version of the first and second registers loosely arranged by folio number but contains different improvement information than the same folio pages.

*The Fourth Register is an unbound (loose paper) version of the plans that are associated with the Third Register.

*The Fifth Register includes a note to assist in the correct interpretation as to the Properties Register recording Land and Building Costs followed by a table with a register of Buildings and Land valuations. With the Index following in sections A-W with reference to Folio Number 8601-8850.
The Register sheets included Description of Property, Titles (Volume, Folio) Date of Registration, Consideration, Land [Value], Building, Less Adjustments, Total Value and Remarks. It includes Bus Depots, Shelters and Signal Cabins, and Sale/Demolition notices.

*The Sixth volume is a Property List containing an Index/Summary by numbers 1-8 giving reference to Map Number and Drawing Number of property locations.
1 - Tram Depots, Bus Garages, Workshops, Clothing Factory and Store, Store
2 - Head Office, Wattle Park, Meal Rooms, Signals Cabins
3 - Substations
4 - Rented Properties
5 - Shelters - Scheduled under Districts
6 - Shelters - Scheduled under Numbers
7 - Traffic Toilets
8 - Bus Water Standards

The CDFW Owned and Operated Lands and Conservation Easements dataset is a subset of the CDFW Lands dataset. It contains lands owned (fee title), some operated (wildlife areas, ecological reserves, and public/fishing access properties that are leases/agreements with other agencies that may be publicly accessible) and conservation easements held by CDFW. CDFW Owned and Operated Lands and Conservation Easements replaces the prior dataset, DFG Owned and Operated Lands, which included only fee title lands and some operated lands (wildlife areas, ecological reserves, and public/fishing access properties that are leases/agreements with other agencies and that may be publicly accessible). This is a generalized version dataset that has a shorter attribute table than the original and also has been dissolved based on the fields included. Please note that some lands may not be accessible due to the protection of resources and habitat. It is recommended that users contact the appropriate regional office for access information and consult regulations for CDFW lands in Sections 550, 550.1, 551, 552, 630 and 702. The CDFW Lands dataset is a digitized geographical inventory of selected lands owned and/or administered by the California Department of Fish and Wildlife. Properties such as ecological reserves, wildlife areas, undesignated lands containing biological resource values, public and fishing access lands, and CDFW fish hatcheries are among those lands included in this inventory. Types of properties owned or administered by CDFW which may not be included in this dataset are parcels less than 1 acre in size, such as fishing piers, fish spawning grounds, fish barriers, and other minor parcels. Physical boundaries of individual parcels are determined by the descriptions contained in legal documents and assessor parcel maps relating to that parcel. The approximate parcel boundaries are drawn onto U.S. Geological Survey 7.5'-series topographic maps, then digitized and attributed before being added to the dataset. In some cases, assessor parcel or best available datasets are used to digitize the boundary. Using parcel data to adjust the boundaries is a work in progress and will be incorporated in the future. Township, range, and section lines were based on the U.S. Geological Survey 7.5' series topographic maps (1:24,000 - scale). In some areas, the boundaries will not align with the Bureau of Land Management's Public Lands Survey System (PLSS). See the "SOURCE" field for data used to digitize boundary.This dataset is intended to provide information on the location of lands owned and/or administered by the California Department of Fish and Wildlife (CDFW) and for general conservation planning within the state. This dataset is not intended for navigational use. Users should contact the CDFW, Wildlife Branch, Lands Program or CDFW Regional offices for access information to a particular property. These datasets do not provide legal determination of parcel acreages or boundaries. Legal parcel acreages are based on County Assessor records. Users should contact the Wildlife Branch, Lands Program for this information and related data. When labeling or displaying properties on any map, use the provided field named "MAPLABEL" or use a generic label such as "conservation lands", "restricted lands", or some other similiar generalized label. All conservation easements are closed to public access.This dataset is not a surveyed product and is not a legal record of original survey measurements. They are representations or reproductions of information using various sources, scales, and precision of boundary data. As such, the data do not carry legal authority to determine a boundary, the location of fixed works nor is it suitable for navigational purposes. The California Department of Fish and Wildlife shall not be held liable for any use or misuse of the data. Users are responsible for ensuring the appropriate use of the data . It is strongly recommended that users acquire this dataset directly from the California Department of Fish and Wildlife and not indirectly through other sources which may have outdated or misinterpreted information.

All data compiled into this dataset is available under public domain. This set is designed to provide some insight into sales trends across the state of Connecticut as well as the individual towns within. It is also specifically structured to highlight changes in trends due to the COVID-19 pandemic.

Variables

list_year: grand list year of the property (grand list years run from Oct. 1 through Sept. 30). town: name of the town that the property was sold in. population: population of the town that the property was sold in. residential_type: single family, two family, three family, four family, or condo. month: the month the sale was recorded. year: the year the sale was recorded. in_pandemic: boolean value indicating whether the selling date was after March 11, 2020. assessed_value: tax assessed value of the property at the time of the sale. sale_amount: final closing sale amount of the property. price_index: the Consumer Price Index (CPI) for that month/year. Used to normalize dollar values. norm_assessed_value: CPI-normalized assessed value (assessed_value / price_index * 100). norm_sale_amount: CPI-normalized sale amount (sale_amount / price_index * 100). norm_sales_ratio: CPI-normalized assessment to sale ratio (norm_assessed_value / norm_sale_amount). latitude: latitude for the property's town. longitude: longitude for the property's town.

Note: the original dataset also contained the street address and exact sale date for each record. Those variables were removed as they were not relevant to the analysis being conducted and to afford the individuals associated with each sale a stonger degree of personal privacy. Records from October 2000 to October 2010 from the original dataset were omitted due to timeliness issues. Records of non-residential types were omitted as they lacked enough historic records to be of consequence to the analysis.

Data sources

Real estate records: https://data.ct.gov/Housing-and-Development/Real-Estate-Sales-2001-2020-GL/5mzw-sjtu Township shapes: https://data.ct.gov/Government/Town-Boundary-Index-Map/evyv-fqzg Consumer price index: https://www.bls.gov/regions/new-england/data/consumerpriceindex_us_table.htm Town populations: https://www.connecticut-demographics.com/cities_by_population

This layer represents current city parcels within the City of Los Angeles. It shares topology with the Landbase parcel lines feature class. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way, ownership and land record information. The legal boundaries are determined on the ground by license surveyors in the State of California, and by recorded documents from the Los Angeles County Recorder's office and the City Clerk's office of the City of Los Angeles. Parcel and ownership information are available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the landbase parcels is entered into attributes. Principal attributes include:PIN and PIND: represents the unique auto-generated parcel identifier and key to related features and tables. This field is related to the LA_LEGAL, LA_APN and LA_HSE_NBR tables. PIN contains spaces and PIND replaces those spaces with a dash (-).LA_LEGAL - Table attributes containing legal description. Principal attributes include the following:TRACT: The subdivision tract number as recorded by the County of Los AngelesMAP_REF: Identifies the subdivision map book reference as recorded by the County of Los Angeles.LOT: The subdivision lot number as recorded by the County of Los Angeles.ENG_DIST: The four engineering Districts (W=Westla, C=Central, V= Valley and H=Harbor).CNCL_DIST: Council Districts 1-15 of the City of Los Angeles. OUTLA means parcel is outside the City.LA_APN- Table attributes containing County of Los Angeles Assessors information. Principal attributes include the following:BPP: The Book, Page and Parcel from the Los Angeles County Assessors office. SITUS*: Address for the property.LA_HSE_NBR - Table attributes containing housenumber information. Principal attributes include the following:HSE_ID: Unique id of each housenumber record.HSE_NBR: housenumber numerical valueSTR_*: Official housenumber addressFor a complete list of attribute values, please refer to Landbase_parcel_polygons_data_dictionary.

© Randy Price Division Manager Mapping and Land Records Division Bureau of Engineering / Department of Public Works City of Los Angeles This layer is sourced from lacitydbs.org

Georeferenced Amsterdam Rental Values

Exploring Urban Housing Markets Through Historical Patterns

By [source]

About this dataset

This dataset provides insightful and comprehensive information on the spatial distribution of rental values in Amsterdam throughout a period of time. In order to generate this data, the Verponding registration from Amsterdam City Archives was consulted, which collected a tax known as the Verpondings-quohieren van den 8sten penning on the rental value of immovable property. This data was attained through transcribing and geo-referencing registration books from the archives; thereby incorporating both transcribed rental values of all real estate properties listed therein as well as geo-referenced tax records plotted onto an historical map of Amsterdam.

The compilation and analysis of historic rental values may offer further insights into underlying social, economic, and cultural developments within Amsterdam over time. Therefore, the potential applications for this dataset are enormous; offering investigators an opportunity to gather useful information with relation to urban renewal efforts or even supporting archaeological research studies. Moreover, with various columns such as order number, wijk district where applicable property is located within respective street name as well as details on whether said property is available for rent/own disposition - researchers may also utilize these collected metrics for meaningful planning/management decisions simultaneously unfolding hidden patterns concerning disparities or trends that might be discerned when compared to current trends employed by residents today

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How to use the dataset

This dataset provides insight into the spatial distribution of rental values in Amsterdam between 1647 and 1652. The data provided is a valuable resource for researchers looking to study the economic, social, and cultural history of Amsterdam over this period in time. With this data set, users can explore hidden patterns, disparities, and trends that may inform decision-making or help with urban renewal projects. Moreover, this dataset can also be used to assess archaeological and cultural heritage research.

In order to understand the georeferenced rental values better and draw meaningful conclusions from the data set it is important to keep few things in mind: - Check out handy columns such as ‘wijk’ (district) which offers information about where each property is located;
- The ‘rent/own’ indicates whether a property was rented (huur) or owned (koop);
- The ‘value’ column contains information regarding the rental value of each property; - The ‘tax’ column shows how much tax was paid on each listed property;
- In addition to this additional notes have been provided in some cases offering more insights into particular properties;

By seeing all these details together one will get an excellent overview of individual households renting or owning their real estate properties along with their tax payment throughout Amsterdam during this period 1647-1652. Additionally by graphing this data one could compare rental value against geographic location or even track consecutive years on how they vary year after year! This can help trace any historical changes taking place how they affect individual households within Amsterdam as well as socio-economic changes occurring throughout the city over the years!

Research Ideas

• Creating a predictive heat map by analyzing correlation between rental values and various other factors such as geographic location, proximity to public transportation, availability of amenities/services etc.
• Comparing and contrasting current maps of real estate prices in Amsterdam with the maps from this dataset to analyze shifts in property prices over time and understand the evolution of urban housing markets in the city.
• Studying socio-economic differences between different geographical areas based on rental values from this dataset, which could help provide a better understanding of the social, economic, and cultural history of the city

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permi...

Current resister of community based assets (buildings and land) owned by Glasgow City Council and associated bodies. These may include but not be limited to community centres, local halls and play areas.

This dataset identifies property managed partially or solely by NYC Parks. This data has been produced in whole or part using secondary data. Data accuracy is limited by the scale and accuracy of the original sources. Site-specific conditions should be field-verified.

Records are added as more land is designated under NYC Parks’ jurisdiction. Each record represents an acquisition.

User Guide: https://docs.google.com/document/d/1NExNJF5YKID04oOopi0fHainRuGG3Pz_jKSrMujPsPk/edit?usp=sharing

Data Dictionary: https://docs.google.com/spreadsheets/d/1Q4DBWu7riNFxWvy1vnTJHoOI3r2L9oW6eCN56jCNyCw/edit?usp=sharing

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

This dataset combines Brisbane City Council property information with the Queensland Government Digital Cadastral Database (DCDB) in Brisbane City Council area.

Land Parcels are the building blocks of Council properties. Land parcels (also called lots) are mapped and the title details shown on a Plan of Subdivision. The parcel is a graphical representation of surveyed boundaries together with identifiers such as Lot/Plan description and house numbers.

The Digital Cadastral Database (DCDB) is the spatial representation of every current parcel of land in Queensland, and its legal Lot on Plan description and relevant attributes. It provides the map base for systems dealing with land related information. The DCDB is considered to be the point of truth for the graphical representation of property boundaries. It is not the point of truth for the legal property boundary or related attribute information, this will always be the plan of survey or the related titling information and administrative data sets.

Warning. Downloading this entire dataset in shapefile format exceeds the current 2GB download limit set by ESRI. Information from ESRI has the following suggestions. Consider the following options: Output to a file geodatabase instead of a shapefile or Process the data in sections.