In 2025, around ** percent of people said the higher prices were going to impact their plans on Valentine's and/ or Galentine's Day. ** percent of people said it would not change their plans.

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

According to a survey taken in July 2025, roughly 27percent of surveyed Americans were planning to make purchases because they expected prices to increase as a result of the tariffs.

The controversy over how much to charge for health products in the developing world rests, in part, on whether higher prices can increase use, either by targeting distribution to high-use households (a screening effect), or by stimulating use psychologically through a sunk-cost effect. We develop a methodology for separating these two effects. We implement the methodology in a field experiment in Zambia using door-to-door marketing of a home water purification solution. We find evidence of economically important screening effects. By contrast, we find no consistent evidence of sunk-cost effects. (JEL C93, D12, I11, M31, O12)

This dataset contains the predicted prices of the asset Pepe Goes Higher 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.

1. Understanding the Features

Brand and Model: Analyze how different brands and models influence car prices. Are luxury brands significantly more expensive than economy brands? Year of Manufacture: Discuss the depreciation of car prices over time. How does the year affect pricing, and are there notable trends for specific brands? Engine Size: Explore the relationship between engine size and price. Does a larger engine correlate with a higher price, and how does this vary across different fuel types? Fuel Type: Evaluate how fuel types (Petrol, Diesel, Electric, Hybrid) impact pricing. Are electric vehicles priced higher due to their technology, or do they vary based on other factors? Transmission: Discuss if manual or automatic transmissions affect car pricing, especially in different markets or demographics.

2. Price Predictions and Modeling

Machine Learning Models: Explore which models (e.g., linear regression, decision trees, or ensemble methods) are best suited for predicting car prices using this dataset. Feature Importance: Discuss the importance of different features in predicting price. Which features contribute most to the price prediction accuracy, and how can feature selection improve the model?

3. Market Trends and Insights

Price Distribution: Analyze the distribution of car prices. Are there a lot of high-priced luxury cars, or is the dataset skewed towards more affordable options? Mileage vs. Price: Investigate the correlation between mileage and price. How does higher mileage affect pricing, and is there a threshold where price reduction becomes significant? Condition Impact: Discuss how the condition of the car (New, Used, Like New) influences the price. Are there significant price drops for used cars compared to new ones?

4. Regional Analysis

Location Impact: If geographic location is included, discuss how prices vary by region. Are there markets where certain brands/models are more popular and thus command higher prices? Economic Factors: Consider how broader economic factors (like inflation, fuel prices, and consumer preferences) might influence car prices in different regions.

5. Future Developments and Trends

Electric Vehicle Market: With the rise of electric vehicles, discuss how this dataset reflects the growing demand and pricing trends for EVs compared to traditional fuel cars. Impact of Technology: Consider how advancements in technology, safety features, and autonomous driving capabilities might influence future pricing.

6. Limitations of the Dataset

Data Completeness: Discuss any potential limitations in the dataset, such as missing values or biases in the data collection process. Generalization: Reflect on the ability to generalize the findings from this dataset to broader car markets or regions. Are there potential confounding factors that should be considered?

7. Potential Applications

Pricing Strategies: How can dealerships or private sellers utilize insights from this dataset to set competitive pricing? Consumer Decision-Making: Discuss how consumers can leverage this dataset to make informed purchasing decisions based on price predictions and feature evaluations.

These discussion points can help guide deeper analysis and insights into the Car Price Prediction dataset, making it a valuable resource for both academic and practical applications. If you have specific areas you want to focus on, let me know!

Between January 2023 and December 2024, a large share of consumers in Brazil expected higher prices when grocery shopping across all age ranges. While perception varied over the period, as of December 2024, people aged 55 and over were the ones expected grocery inflation the most.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for higher on 2025-12-12. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

In the second quarter of 2025, North Macedonia, Portugal, and Bulgaria registered the highest house price increase in real terms (adjusted for inflation). In North Macedonia, house prices outgrew inflation by nearly ** percent. When comparing the nominal price change, which does not take inflation into consideration, the average house price growth was even higher.

Meanwhile, many countries experienced declining prices, with Hong Kong recording the biggest decline, at ***** percent. That has to do with a broader trend of a slowing global housing market.

Graph and download economic data for Home Price Index (High Tier) for Portland, Oregon (POXRHTNSA) from Jan 1987 to Sep 2025 about high tier, Portland, HPI, housing, price index, indexes, price, and USA.

This dataset contains the predicted prices of the asset Grip Harder. Climb Higher. 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.

Graph and download economic data for Home Price Index (High Tier) for Washington D.C. (WDXRHTNSA) from Jan 1987 to Sep 2025 about high tier, Washington, HPI, housing, price index, indexes, price, and USA.

ISM Manufacturing Prices in the United States increased to 58.50 points in November from 58 points in October of 2025. This dataset includes a chart with historical data for the United States ISM Manufacturing Prices Paid.

Graph and download economic data for Home Price Index (High Tier) for San Francisco, California (SFXRHTSA) from Jan 1987 to Sep 2025 about high tier, San Francisco, HPI, housing, price index, indexes, price, and USA.

Consumer Price Index (CPI): South Minahasa Regency: Education: Higher Education data was reported at 100.000 2022=100 in Apr 2025. This stayed constant from the previous number of 100.000 2022=100 for Mar 2025. Consumer Price Index (CPI): South Minahasa Regency: Education: Higher Education data is updated monthly, averaging 100.000 2022=100 from Jan 2023 (Median) to Apr 2025, with 28 observations. The data reached an all-time high of 100.000 2022=100 in Apr 2025 and a record low of 100.000 2022=100 in Apr 2025. Consumer Price Index (CPI): South Minahasa Regency: Education: Higher Education data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IC116: Consumer Price Index: by Regency and Municipality: Sulawesi: South Minahasa.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q3 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

This dataset represents typical financial time series data related to stock prices. Each column represents a specific type of information:

Date: The date on which the stock prices are recorded.

Open: The price of the stock at the beginning of the trading day (when the market opens).

High: The highest price of the stock during the trading day.

Low: The lowest price of the stock during the trading day.

Close: The price of the stock at the end of the trading day (when the market closes).

Adj Close (Adjusted Close): The closing price of the stock adjusted for dividends, stock splits, and other corporate actions. This provides a more accurate measure of the stock's performance for investors.

Volume: The number of shares traded on that particular day. In your data, some days have a volume value of '0', which might indicate a lack of data or no trading activity on that day.

Gold prices are essential for economic analysis and investment decisions. Gold is often seen as a safe-haven asset, especially during periods of market uncertainty. This data is used for technical analysis, trend analysis, and market forecasting.

Graph and download economic data for Home Price Index (High Tier) for Denver, Colorado (DNXRHTSA) from Jan 1987 to Sep 2025 about high tier, Denver, HPI, housing, price index, indexes, price, and USA.

This dataset contains the predicted prices of the asset goes higher with every bid 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.

Housing Inventory: Price Increased Count Year-Over-Year in Lehigh County, PA was -66.67% in August of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Price Increased Count Year-Over-Year in Lehigh County, PA reached a record high of 1,400.00 in March of 2021 and a record low of -87.50 in February of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Price Increased Count Year-Over-Year in Lehigh County, PA - last updated from the United States Federal Reserve on October of 2025.