Actual price registration information for real estate sales cases, including target location (de-identified), area, total price and other information.

Actual price registration information for real estate sales cases, including target location (de-identified), area, total price and other information.

Actual price registration information for real estate sales cases, including target location (de-identified), area, total price and other information.

Vehicle registration marks sales through public auction and the associated hammer price from 1998 - 2020. No public auctions were held between 2006 - 2012.

Used Cars

Analyzing Seller Type, Vehicle Type and Year of Registration

By Data Society [source]

About this dataset

The autos.csv dataset is a comprehensive collection of valuable data about used cars, and provides insight into how the cars are being sold, what price they are being sold for, and all the details about their condition. Each ad contains information such as dateCrawled (the date the ad was first seen), name of the car, seller type (private or dealer), offer type, price, A/B testing information , vehicle type, year of registration (at which year was the car first registered) , gearbox type, power output in PS (horsepower) , model of car , howmany kilometers has it driven so far , monthof registration(when it was first registered)(essentially giving us an idea about its age), fueltype utilized by it( petrol/diesel /electricity/lpg etc.), brand name to which it belongs to notRepairedDamage - if there is any damage on the vehicle that has not been repaired yet. DateCreated gives us information when this particular advertisement was created in ebay or other place where these cars can be posted. The nrofpictures field will give you an estimate regarding how many images have been included with this ad and postalcode contain info regarding area code where car have been posted.. Lastly lastseen give us time estimation when a crawler last scan this particular post online .All these factors are instrumental in determining a suitable price for used vehicles . Meanwhile regression analysis based on average prices related to years can be done from this dataset .So grab your laptop get ready !!!

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How to use the dataset

This dataset is a great resource to begin exploring the factors that affect used car prices. With features such as dateCrawled, name, seller, offerType, price, abtest among other data points it can be used to uncover how different aspects of a vehicle determine the pricing of second hand cars.

The first step would be to explore and understand what each of these fields represent and have an idea about their importance when pricing a used car. One might then proceed by plotting distribution plots for numerical variables such as yearOfRegistration with price or bar graphs for categorical fields like fuelType to observe if there is any correlation with price in these variables. Knowing certain key trends can assist in predicting future market prices more accurately than relying on yearly averages of all car values combined - which might give shapes too broad general trends instead precise predictions when working with this dataset alone.

In addition understanding how long a listing lasts before being sold would give valuable insight into discover how competitive offers should stay when customers come across relevant listings on say ebay or other trading sites that list used cars; this could achieved by utilizing two columns - lastSeen and dateCrawled - to figure out their average lifespan before they were sold out. It's likely that its higher priced counterparts tend to remain listed longer than cheaper listings which quickly disappear after being seen often enough by members in related markets searching those platforms for new vehicles up for sale at any given time within certain parameters established such as location or age amongst others .

Finally one might use supervised learning algorithms such as Linear Regression or Random Forest coupled with feature engineering methods like PCA (Principal Component Analysis) aiming at reducing high dimensionality issues on datasets composed mostly of categorical variables so we can perform actual machine learning operations over extracted numerical feature columns from processes along those lines previously mentioned

Research Ideas

• Analyze the relationship between car prices and age (year of registration) using a linear regression model to suggest which cars provide the best value for money.
• Use classification models to predict vehicle types based on features like powerPS, price, brand, etc.
• Compare and contrast seller types (private vs dealer) by analyzing prices, seller locations and other geographic information in order to give advice on which type of seller provides the best deals for customers

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistri...

Dataset

This dataset was created by Mukesh

Contents

The register covers the whole country and covers all types of real estate. The information on a transfer may include, for example, the purchase price, the date of purchase, the buyer and the seller. The register contains only full acquisitions. In addition to transfers by purchase, the land price register contains information on transfers made by inheritance, exchange or gift. The register contains transfers entered in 2014-09-20 and later. The dataset cannot be ordered separately but is only part of existing services: Property price notification and property price withdrawals

Dataset

This dataset was created by Brendan Hayes

Released under CC0: Public Domain

Contents

The “Registration&Turnout.dta” dataset is the one from the main door to door study using data from administrative records and voter lists.

The “Survey.dta” dataset is the result of the post electoral survey mentioned in the paper. You’ll find the corresponding questionnaire in the main folder.

From How to access HM Land Registry Price Paid Data

Price Paid Data tracks property sales in England and Wales submitted to HM Land Registry for registration. Price Paid Data is based on the raw data released each month.

Brazil Construction Materials: Price: Registration of Pressure Chromed D=1/2 data was reported at 49.134 BRL/Unit in May 2019. This records a decrease from the previous number of 50.186 BRL/Unit for Apr 2019. Brazil Construction Materials: Price: Registration of Pressure Chromed D=1/2 data is updated monthly, averaging 39.202 BRL/Unit from Feb 2007 (Median) to May 2019, with 148 observations. The data reached an all-time high of 50.186 BRL/Unit in Apr 2019 and a record low of 29.441 BRL/Unit in Feb 2007. Brazil Construction Materials: Price: Registration of Pressure Chromed D=1/2 data remains active status in CEIC and is reported by Brazilian Chamber of Construction Industry. The data is categorized under Brazil Premium Database’s Construction and Properties Sector – Table BR.EF001: Construction Materials: Price.

Price Paid Data tracks the residential property sales in England and Wales that are lodged with Land Registry for registration.

Our price paid data tracks the residential property sales in England and Wales that are lodged with us for registration. The dataset is a reliable source of house price information and consists of more than 24 million definitive records dating back to January 1995. For more information on this dataset and what it does and doesn't include, visit https://www.gov.uk/about-the-price-paid-data

Choose from three options to select the data that best meets your requirements:

monthly file: contains a single monthly file of the transactions received in the period from the first to the last day of the corresponding month, including any changes or deletions to previously downloaded data. The data is updated monthly and the average size of this file is 11 MB.

single file: contains all the up to date data from 1995 to the current date. The data is updated monthly and the average size of this file is 2.86 GB.

yearly files: contains annual files of up to date data, ranging from 1995 to the current date. Unlike the monthly files described above, yearly files are collated on the date of the transaction/deed date rather than the date that the information was lodged with Land Registry. The data is updated monthly and the sizes of these files range from 87 MB to 222 MB. If you are having trouble downloading any of the year files in full, they are also available as two smaller, evenly split CSV files.

We strive to ensure that our public data is as accurate as possible but cannot guarantee that it is free from errors or fit for your purpose or use. Reports are based on data collected at the time a property transaction is registered with us and will not necessarily be up to date with the most recent information. See https://www.gov.uk/government/publications/land-registry-data/public-data#accuracy-of-the-data for more information.

The property price register is kept on the basis of prices specified in notarial deeds. The following are also subject to registration: the location of the property, the numbers of the land plots included in the property, the type of property (with the distinction of undeveloped agricultural real estate, developed agricultural real estate, undeveloped real estate intended for development other than homesteads, real estate built on residential buildings, real estate built on buildings performing other functions than homesteads and housing, building real estate, residential real estate), the area of land property, the date of conclusion of a notarial deed or determination of value, other available data on real estate and its components.

United States CPI UW: Transport: Private: MF: State & Local Registration & License data was reported at 0.278 % in Jun 2018. This records a decrease from the previous number of 0.279 % for May 2018. United States CPI UW: Transport: Private: MF: State & Local Registration & License data is updated monthly, averaging 0.317 % from Jan 1998 (Median) to Jun 2018, with 246 observations. The data reached an all-time high of 0.430 % in Jan 1998 and a record low of 0.276 % in Jul 2008. United States CPI UW: Transport: Private: MF: State & Local Registration & License data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I010: Consumer Price Index: Urban: Weights.

United States CPI U: Transport: Private: MF: State & Local Registration & License data was reported at 178.703 Dec1997=100 in Oct 2018. This records an increase from the previous number of 178.489 Dec1997=100 for Sep 2018. United States CPI U: Transport: Private: MF: State & Local Registration & License data is updated monthly, averaging 141.593 Dec1997=100 from Dec 1997 (Median) to Oct 2018, with 251 observations. The data reached an all-time high of 178.746 Dec1997=100 in Aug 2018 and a record low of 100.000 Dec1997=100 in Dec 1997. United States CPI U: Transport: Private: MF: State & Local Registration & License data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I002: Consumer Price Index: Urban.

Actual price registration information for real estate sales cases, including target location (de-identified), area, total price and other information.

Thailand Consumer Price Index (CPI): Rural: TC: Registration Fees and Auto Insurance: Registration data was reported at 100.000 2015=100 in Jun 2018. This stayed constant from the previous number of 100.000 2015=100 for May 2018. Thailand Consumer Price Index (CPI): Rural: TC: Registration Fees and Auto Insurance: Registration data is updated monthly, averaging 100.000 2015=100 from Jan 2002 (Median) to Jun 2018, with 198 observations. The data reached an all-time high of 100.000 2015=100 in Jun 2018 and a record low of 99.600 2015=100 in Dec 2008. Thailand Consumer Price Index (CPI): Rural: TC: Registration Fees and Auto Insurance: Registration data remains active status in CEIC and is reported by Bureau of Trade and Economic Indices. The data is categorized under Global Database’s Thailand – Table TH.I008: Consumer Price Index: 2015=100: Rural.

Blockchain data query: .eth registration gas price and count scatterplot grouped by year