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Nigerian House Price Dataset This dataset provides a comprehensive look at housing prices across various towns and states in Nigeria. It contains key features that influence property values. The variable in the dataset are:
bedrooms: Number of bedrooms in the property bathrooms: Number of bathrooms available toilets: Number of toilets available parking_space: Availability of parking spaces (measured in number of cars accommodated) title: This variable represent the house type town: The town where the property is located state: The state in Nigeria where the property is located ****price:**** The listed price of the property in Nigerian Naira (₦)
This dataset is valuable for analyzing real estate trends, predicting housing prices, and understanding the factors that drive property valuation in Nigeria. It offers insights into the housing market across different regions, making it a useful resource for data scientists, analysts, and real estate professionals.
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Graph and download economic data for Price Level of Imports for Nigeria (PLMCPPNGA670NRUG) from 1950 to 2019 about Nigeria, imports, and price.
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Explore this dataset, a vibrant part of the Nigeria Data Grid, offering a comprehensive view of Food Prices in Nigeria. Curated from the World Food Programme Price Database, it covers essentials like maize, rice, beans, fish, and sugar.
Unravel market dynamics, analyze trends, and gain unique insights for research, policymaking, and a nuanced understanding of the intricate tapestry of food pricing in Nigeria.
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Nigeria: Housing and utilities price index, world average = 100: The latest value from 2021 is 51.54 index points, a decline from 51.963 index points in 2017. In comparison, the world average is 77.639 index points, based on data from 165 countries. Historically, the average for Nigeria from 2017 to 2021 is 51.752 index points. The minimum value, 51.54 index points, was reached in 2021 while the maximum of 51.963 index points was recorded in 2017.
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This dataset contains a rich collection of car sale listings scraped from Jiji.ng, one of Nigeria’s largest online marketplaces for buying and selling. It provides valuable insights into the Nigerian car market, including pricing trends, popular brands, engine specifications, and more.
With over 1200 listings, this dataset is ideal for data analysis, price prediction models, trend analysis, and vehicle valuation studies.
🔹 Car Price Prediction – Build ML models to estimate the price of a car based on its features.
🔹 Market Analysis – Study trends in Nigeria’s used car market.
🔹 Customer Preference Analysis – Find out which car models and brands are in high demand.
🔹 Business Insights – Helps car dealers make informed pricing and inventory decisions.
🚀 Train a linear regression model to predict car prices based on attributes.
📊 Use visualization tools to analyze car listings by brand, location, and price.
🛠️ Create an interactive dashboard to explore trends in the Nigerian car market.
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TwitterThis dataset contains the predicted prices of the asset NIGERIA over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.
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Graph and download economic data for Consumer Price Index for Nigeria (DDOE01NGA086NWDB) from 1960 to 2017 about Nigeria, CPI, price index, indexes, and price.
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Nigeria GDP: Market Prices: Compensation of Employees data was reported at 29,944,058.561 NGN mn in 2017. This records an increase from the previous number of 25,813,374.354 NGN mn for 2016. Nigeria GDP: Market Prices: Compensation of Employees data is updated yearly, averaging 1,738,996.040 NGN mn from Dec 1981 (Median) to 2017, with 37 observations. The data reached an all-time high of 29,944,058.561 NGN mn in 2017 and a record low of 120,851.386 NGN mn in 1981. Nigeria GDP: Market Prices: Compensation of Employees data remains active status in CEIC and is reported by National Bureau of Statistics of the Federal Republic of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.A014: GDP: by Income: Current Price: Annual.
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Nigeria: Food price index, world average = 100: The latest value from 2021 is 101.71 index points, an increase from 84.436 index points in 2017. In comparison, the world average is 105.854 index points, based on data from 165 countries. Historically, the average for Nigeria from 2017 to 2021 is 93.073 index points. The minimum value, 84.436 index points, was reached in 2017 while the maximum of 101.71 index points was recorded in 2021.
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TwitterThis dataset contains Food Prices data for Nigeria, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets.
Source: FEWSNET via FAO: GIEWS, FPMA, Nigeria, SIMA - Niger, WFP Contributor: WFP - World Food Programme License: Creative Commons Attribution for Intergovernmental Organisations (CC BY-IGO)
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TwitterFood price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.
A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.
The data cover the following sub-national areas: Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Adamawa, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Market Average
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Consumer Price Index CPI in Nigeria increased to 128.90 points in October from 127.70 points in September of 2025. This dataset provides - Nigeria Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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TwitterIn September 2023, the Consumer Index Price of food in Nigeria stood at 737.3, increasing from the previous year. Consumer price index is a measure that examines the changes in the purchasing power of a currency. It measures changes in the price level of the market basket of consumer goods and services purchased by households. The movement of the Consumer Price Index is the main measure for inflation rate.
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Nigeria Average Fuel Price: Petrol data was reported at 145.701 l in Jan 2019. This records a decrease from the previous number of 145.777 l for Dec 2018. Nigeria Average Fuel Price: Petrol data is updated monthly, averaging 147.773 l from Jan 2017 (Median) to Jan 2019, with 24 observations. The data reached an all-time high of 190.875 l in Jan 2018 and a record low of 109.600 l in Jan 2017. Nigeria Average Fuel Price: Petrol data remains active status in CEIC and is reported by National Bureau of Statistics of the Federal Republic of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.P003: Average Fuel Price.
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Graph and download economic data for Real Effective Exchange Rate as Based on Consumer Price Index for Nigeria (NGAEREERIX) from 2000 to 2024 about Nigeria, consumer prices, REO, exchange rate, consumer, real, rate, and indexes.
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TwitterIn 2018, the average price of smartphones available in Nigeria on the online marketplace Jumia was ** U.S. dollars. That year, the average price in Nigeria reached the African average figure. In the previous years, smartphones on Jumia were more expensive than those available in other African countries.
In 2019, Jumia was by far the most popular online marketplace for physical goods in Nigeria. Other common marketplaces were Jiji, Konga, and Cheki.
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Nigeria: Transport prices, world average = 100: The latest value from 2021 is 62.86 index points, a decline from 67.47 index points in 2017. In comparison, the world average is 92.43 index points, based on data from 165 countries. Historically, the average for Nigeria from 2017 to 2021 is 65.17 index points. The minimum value, 62.86 index points, was reached in 2021 while the maximum of 67.47 index points was recorded in 2017.
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Looking for a place to rent or buy a house in Lagos, Nigeria? This dataset can help you identify the best places to live in the city! It includes information on housing prices, property names, addresses, and neighborhoods. With this data, you can figure out which areas are the most (and least) profitable for renting or buying a home. So whether you're looking for your next home or investment property, this dataset is a great resource
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If you're looking for information on housing prices in Lagos, Nigeria, this dataset is a great resource. It includes the price, property name, address, and neighborhood for each listing. This information can be used to identify the most and least profitable neighborhoods in Lagos
- This dataset can be used to identify the most and least expensive neighborhoods in Lagos, Nigeria.
- This dataset can be used to identify the best places to rent or buy a house in Lagos, Nigeria.
- This dataset can be used to identify the most and least profitable neighborhoods in Lagos for real estate investors
If you use this dataset in your research, please credit the original authors.
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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 permission. See Other Information.
File: d.csv | Column name | Description | |:------------------|:--------------------------------------------| | Price | The price of the housing listing. (Numeric) | | Property_name | The name of the property. (String) | | Address | The address of the property. (String) |
File: rent.csv | Column name | Description | |:------------------|:--------------------------------------------| | Price | The price of the housing listing. (Numeric) | | Property_name | The name of the property. (String) | | Address | The address of the property. (String) |
File: rent_2.csv | Column name | Description | |:------------------|:--------------------------------------------| | Price | The price of the housing listing. (Numeric) | | Property_name | The name of the property. (String) | | Address | The address of the property. (String) |
File: sale.csv | Column name | Description | |:------------------|:--------------------------------------------| | Price | The price of the housing listing. (Numeric) | | Property_name | The name of the property. (String) | | Address | The address of the property. (String) |
File: sale_2.csv | Column name | Description | |:------------------|:--------------------------------------------| | Price | The price of the housing listing. (Numeric) | | Property_name | The name of the property. (String) | | Address | The address of the property. (String) |
If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .
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TwitterEnergy price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes energy price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.
The data cover the following sub-national areas: Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Market Average
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Nigeria GDP: Basic Prices: Services: IC: Publishing data was reported at 32,231.880 NGN mn in 2017. This records an increase from the previous number of 29,820.962 NGN mn for 2016. Nigeria GDP: Basic Prices: Services: IC: Publishing data is updated yearly, averaging 1,897.050 NGN mn from Dec 1981 (Median) to 2017, with 37 observations. The data reached an all-time high of 32,231.880 NGN mn in 2017 and a record low of 58.178 NGN mn in 1981. Nigeria GDP: Basic Prices: Services: IC: Publishing data remains active status in CEIC and is reported by National Bureau of Statistics of the Federal Republic of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.A007: GDP: by Industry: Current Price: Annual.
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Nigerian House Price Dataset This dataset provides a comprehensive look at housing prices across various towns and states in Nigeria. It contains key features that influence property values. The variable in the dataset are:
bedrooms: Number of bedrooms in the property bathrooms: Number of bathrooms available toilets: Number of toilets available parking_space: Availability of parking spaces (measured in number of cars accommodated) title: This variable represent the house type town: The town where the property is located state: The state in Nigeria where the property is located ****price:**** The listed price of the property in Nigerian Naira (₦)
This dataset is valuable for analyzing real estate trends, predicting housing prices, and understanding the factors that drive property valuation in Nigeria. It offers insights into the housing market across different regions, making it a useful resource for data scientists, analysts, and real estate professionals.