Understanding Housing Affordability: Key Metrics and Statistics

Understanding Housing Affordability: Key Metrics and Statistics

Mar 11, 2025 | Data Domain, Inference and Analytics

Housing affordability is a significant concern in many parts of the world, affecting the quality of life and economic wellbeing of individuals and families.

Professor Nicole Gurran from the School of Architecture, Design and Planning says governments around the world are searching for solutions to fix housing affordability, with two opposing schools of thought seeing the solutions as:

  1. Increasing supply. Those in support of this point of view see housing as more expensive because there’s not enough new supply. They see land use regulation and planning processes as restrictive to new construction, adding costly delays and uncertainty to the development process.
  2. On the flipside, others argue that ‘demand side’ factors underlying global house price inflation, such as low cost credit under financial deregulation, or government incentives to encourage property investment are being ignored. They highlight the political influence of property industry groups sustaining housing demand while advocating for reduced regulations. Some even suggest that extensive rezoning reforms may trigger surges in redevelopment and gentrification, potentially displacing individuals with lower incomes.

To truly understand the dynamics of housing affordability we need to take a detailed look at a range of different metrics and statistics to gain a full picture.

Shedding light on these crucial measures can offer insights for homebuyers, policymakers, real estate professionals, and urban planners.

1. Median and Average Home Prices

These figures provide a baseline for understanding the cost of purchasing a home in a particular area.

The median home price provides a middle point and the average home price presents an overall trend.

2. Price-to-Income Ratio

This critical ratio compares home prices to average household incomes. A higher ratio suggests that homes are less affordable relative to income.

Statista has published the the house price-to-income ratio in Australia as 122.1 as of the third quarter of 2024. This ratio is calculated by dividing nominal house prices by nominal disposable income per head and has increased from the previous quarter.

3. Housing Affordability Measures

A Housing Affordability Index (HAI) assesses whether a typical family can afford the mortgage on a median-priced home, based on their income. An index above 100 indicates greater affordability.

The issue with the HAI is that it primarily focuses on purchase affordability.

The Australian Institute of Health and Welfare (AIHW) broadens what they classify as housing costs in measuring housing affordability.

AIHW defines housing costs as “the sum of rent payments, rate payments (water and general), and housing–related mortgage payments”,

AIHW expresses housing affordability as “the ratio of housing costs to gross household income”,

While housing stress is typically described as “lower-income households that spend more than 30% of gross income on housing costs“.

The second measure is a more comprehensive approach which considers a range of housing costs, the complexity of housing affordability and its impact on households.

4. Rent-to-Income Ratio

Rent-to-Income Ratio compares a tenant’s monthly rent to their gross monthly income expressed as a ratio. For those in the rental market, this ratio measures how much of a household’s income is spent on rent, with higher values indicating less affordability.

PropTrack classifies unaffordable rental housing as a household spending more than a quarter of their pre-tax income on rent. PropTrack recently released its 2025 Rental Affordability Report, and renters are facing the toughest conditions seen in Australia.

Rental property

5. Mortgage Interest Rates

Interest rates directly affect the cost of borrowing money for home purchases.

An increase in mortgage interest rates typically mean an increase in mortgage repayments, which can negatively impact affordability.

While a reduction in rates typically means reduced mortgage repayments, which may improve affordability.

6. Mortgage Payment as a Percentage of Income

Mortgage payment as a percentage of income is an important measure of affordability by demonstrating the burden of mortgage payments relative to a household’s income.

This percentage is calculated by dividing monthly mortgage repayments by gross monthly wages. 

The recommended figure is 28% of pre-tax income. Or in other words, no more than 28% of gross monthly income should go towards monthly mortgage repayments.

7. Homeownership Rates

Broad changes in homeownership rates can signal shifts in affordability, and the overall health of the housing market. 

To gain an idea of homeownership rates in Australia, AIHW shares a view of Home ownership and housing tenure in Australia.

8. Cost of Living

Several measures are published to calculate and help gauge changes in the cost of living. Changes in cost of living impacts our household purchasing power and has implications for housing demand. 

The main ways we measure cost of living is the Consumer Price Index.

Consumer Price Index (CPI)

According to the Australian Bureau of Statistics, CPI is a measure of the average change over time in the prices paid by households for a fixed basket of goods and services (which is grouped into 11 categories: Food and non-alcoholic beverages, Alcohol and tobacco, Clothing and footwear, Housing, Furnishings, household equipment and services, Health, Transport, Communication, Recreation and culture, Education, and Insurance and financial services).

 It’s important to note that the calculation of CPI does not include the cost of buying established dwellings, nor mortgage repayments. However, it does include rents, the cost of new dwellings (excluding value of land) and major alterations and additions to dwellings. 

Included in CPI

Not included in CPI

  • Rent
  • Cost of new dwellings (excluding value of land)
  • Major alterations and additions to dwellings
  • Rates and charges
  • Utilities
  • The cost of buying established dwellings
  • The cost of purchasing land
  • Mortgage repayments
  • Costs associated with servicing a mortgage
Consumer Price Index

9. Gini Coefficient of Home Prices

The Gini Coefficient statistical measure is typically used as a measure of income inequality, although it can be used to assess inequality in various other contexts, including home values in a real estate market.

A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

This measure can indicate the inequality in home values within a market, with higher values suggesting greater disparity.

10. Building Permits and Housing Starts

Building permits and housing starts are indicators of building activity and housing supply.  They can signal future market changes which may impact affordability.

The Proptech Cloud publishes ready-to-use Construction Activity data from the Australian Bureau of Statistics on the Snowflake Marketplace.

11. Vacancy Rates

A vacancy rate is a measure of the percentage of all rental properties that are currently vacant and available for rent.

Fluctuations in vacancy rates can impact rental prices, as elevated rates often correlate with decreased rents, and conversely, lower vacancy rates may lead to higher rental prices. 

12. Debt-to-Income Ratio

An individual’s Debt-to-Income Ratio is calculated by taking their total monthly debt and dividing it by their monthly income.

This ratio reflects a person’s capacity to afford housing in light of their existing debts.

14. Population Growth and Urbanisation

Rapid population growth or accelerated urbanisation can significantly drive up housing demand, often outpacing the supply of available homes. This increased competition for housing can lead to rising property prices and rental costs, making affordability a growing concern, particularly in high-demand urban centres.

Additionally, infrastructure, public services and planning regulations may struggle to keep pace with rapid expansion, further exacerbating affordability challenges. Understanding these dynamics is crucial for policymakers, developers, and investors looking to create sustainable housing solutions..

A Multifaceted View of Housing Affordability

This diverse set of metrics can collectively provide a comprehensive perspective on housing affordability by capturing the various factors that influence pricing while revealing the complexities of the housing market.

These indicators are vital for informed decision-making, policy development and market analysis.

Regularly tracking these metrics allow stakeholders to spot emerging trends, assess market stability and make data-driven decisions.

For those navigating the housing sector, staying informed on these indicators can allow them to anticipate shifts, mitigate risks and capitalise on opportunities in dynamic market.

 

 

Originally published: 18 January, 2024

Last updated: 11 March, 2025

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Understanding Housing Affordability: Key Metrics and Statistics

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The Three Primary Methods of Real Estate Data Integration

The Three Primary Methods of Real Estate Data Integration

Feb 11, 2025 | Technology Application

Real estate data is often fragmented across multiple systems. Learn the three key methods of integration to turn scattered data into actionable insights.

Table of Contents

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Why Integrating Real Estate Data is Critical

Real estate data is highly fragmented. Property details often sit in different systems: land title records, council zoning databases, property listing sites, tenancy records and geospatial datasets.

Without a way to connect this information, we end up with a pile of disconnected data points that fail to tell the full story of a property. This is why effective data integration is so important.

Data integration is the key to transforming raw data into actionable insights. It allows real estate professionals, investors and analysts to derive meaningful conclusions—whether it’s tracking ownership, understanding market trends or making informed investment decisions.

We explain the three primary ways to integrate real estate data: geospatial relationships, title matches and address matching. Each has its own strengths and limitations, depending on the nature of the data and the use case.

1. Geospatial Relationships

What it is: Geospatial integration links data based on location. By using coordinates (latitude and longitude), data points from different sources can be spatially joined.

How it works: When you overlay different datasets—such as zoning maps, property sales records, and infrastructure plans—you can see relationships that wouldn’t be obvious otherwise. For example, a property’s proximity to schools, flood zones, or transport hubs can be determined through geospatial analysis.

Key advantages: This method is highly accurate because geographic coordinates don’t change. Even if an address is incorrectly recorded, a property’s spatial footprint remains fixed.

Limitations: Geospatial integration requires well-defined and accurate location data. If two datasets use different coordinate systems or resolutions, alignment issues can arise. Additionally, real estate data often has legal dimensions that may not be captured purely through geospatial links.

2. Title Matches

What is it: Title matching connects datasets based on property ownership records. This approach relies on land title numbers, which are unique identifiers assigned by government agencies.

How it works: When a property is bought or sold, title details are updated in official registries. Matching records across datasets using title numbers ensures that ownership details, sales history and encumbrances (such as mortgages or caveats) are linked correctly.

Key advantages: Title numbers are unique, making this method highly reliable for tracking ownership and transactions. It’s essential for legal and financial applications, such as mortgage assessments or due diligence for property acquisitions.

Limitations: Title-based integration struggles with temporal changes. Ownership structures change, subdivisions occur, and title references can be updated. If datasets don’t capture changes in sync, they can become misaligned.

3. Address Matching

What it is: Address matching integrates datasets by linking properties based on their address details.

How it works: Addresses are matched across different data sources using structured comparisons. This can involve simple string matching (e.g., “10 Smith Street” vs “10 Smith St”) or more complex approaches that account for variations in formatting, typos, and missing components. Some systems use reference databases, such as the G-NAF (Geocoded National Address File) in Australia, to standardise addresses.

Key advantages: Address matching is often the easiest and most accessible method of integration. It is useful for linking datasets where title information isn’t available, such as real estate listings, valuation reports, or demographic datasets.

Limitations: Address-based integration is prone to inconsistencies. Minor differences in how addresses are recorded can lead to failed matches. Also, properties with multiple units or different access points can create ambiguities. Without standardisation, address matching can result in duplicate or missing records.

When To Use Each Approach

  • Use geospatial relationships when integrating datasets based on physical location, such as infrastructure impact studies, zoning analysis or proximity-based valuations.
  • Use title matches for legally binding property transactions, ownership tracking and financial due diligence.
  • Use address matching when working with customer-facing datasets, listings, or demographic analysis where legal identifiers aren’t available.

In many cases, a combination of these methods provides the most accurate results.

For example, a property data platform might use title matching to ensure ownership accuracy while also using geospatial analysis for insights on location-driven value.

    The Hidden Risk: Ignoring the Temporal Nature of Data

    A common pitfall in real estate data integration is assuming that two datasets referring to the same property at different points in time are still correct when matched. However, real estate is dynamic—ownership can change, addresses updated, properties subdivided and zoning regulations evolve.

    If datasets are not aligned to the same time frame, integration can be technically correct but factually misleading.

    For instance, a dataset showing land ownership in 2022 may not match a property listing from 2024 if the title has since changed hands.

    When integrating data, always consider whether the records represent the same property at the same point in time.

    An Additional Consideration: Just-in-Time vs. Precalculated Integration

    One important factor when integrating real estate data is whether the integration should be performed on demand (just-in-time) or precalculated.

    • Just-in-time integration pulls and matches data when a user requests it. This is useful when dealing with frequently changing datasets, such as live property listings or market analysis tools.
    • Precalculated integration, on the other hand, processes and stores integrated data ahead of time, making it faster to retrieve but potentially outdated.

    Which method is right for your needs? That’s a question we’ll explore in more detail in an upcoming article.

    Assessing Your Data Integration Approach

    If you’re working with real estate data, it’s worth assessing which integration method best suits your needs. Are you working with ownership records that require absolute accuracy? Are you analysing location-based trends? Or do you need to link addresses across multiple systems?

    Each approach has its place, and choosing the right one ensures better insights, fewer mismatches and more reliable decision-making. Fortunately, there are software solutions that specialise in integrating real estate data, as well as consultants who can help design the right approach for your business. If your data integration isn’t working as expected, it may be time to rethink your strategy.

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    What’s the Difference Between GDA94 and GDA2020?

    Feb 11, 2025 | Data Domain

    Geodetic datums, or geodetic systems, are often used by proptechs for mapping  or analysing spatial data.

    Here is a rundown of everything you need to know about the different geodetic datums we use and reference in Australia.

    Table of Contents

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    What is a Geodetic Datum?

    A geodetic datum is a reference framework used to define the Earth’s shape and orientation, providing a coordinate system that allows for accurate mapping, surveying, and pinpointing exact locations on the Earth’s surface.

    In Australia, we use Geodetic Datum of Australia 1994 (DA94) and Geodetic Datum of Australia 2020 (GDA2020).

    The History of Australia’s Geodetic Datums

    Prior to GDA94, Australian surveyors primarily used the Australian Geodetic Datum 1966 (AGD66), which was based on a network of ground-based survey points and astronomical observations.

    AGD66 was the standard datum used for mapping and surveying in Australia for several decades until it was superseded by GDA94 in the 1990s.

    The decision to switch to GDA94 was driven by the need for a more accurate and up-to-date geodetic datum that could take advantage of advances in geospatial technology such as GPS. AGD66 was also affected by tectonic movements and other changes in the Earth’s surface, which made it increasingly difficult to use for accurate positioning and navigation.

    GDA94 (Geocentric Datum of Australia 1994) was the geodetic datum used in Australia from 1994. Based on a mathematical model of the Earth’s surface defined using measurements from a network of ground-based survey points, and used as the standard datum for mapping and surveying in Australia.

    Now, GDA2020 (Geocentric Datum of Australia 2020) is the current geodetic datum used in Australia. It was introduced in 2017 to replace GDA94 and is based on more recent measurements of the Earth’s surface using advanced satellite and ground-based technology.

    GDA2020 provides a more accurate representation of the Earth’s surface than GDA94, and is designed to be compatible with global positioning systems (GPS) and other modern geospatial technologies.

    Even though AGD66, and to some extent GDA94, are no longer the primary datums used in Australia, it’s still important to maintain historical data that was referenced to this datum. And it is possible to transform data from AGD66 to GDA94 or GDA2020 using appropriate transformation parameters to ensure compatibility and accuracy when comparing or integrating data from different sources.

    Conversions between Geodetic Datums

    Conversions between AGD66 and GDA94 are not 100% accurate, because the two datums are based on different mathematical models of the Earth’s surface with different reference points and parameters.

    To convert data from AGD66 to GDA94 (or vice versa), a mathematical transformation must be applied that takes the differences between the two datums into account.

    This transformation involves adjusting the latitude, longitude and height values of the data to align with the new datum.

    However, there are many factors that can affect the accuracy of this transformation, such as:

    1. The quality and accuracy of the original data: If the original data was collected using imprecise or inaccurate methods, the transformation may introduce additional errors or inaccuracies.
    2. The complexity of the transformation: Some transformations may require more complex mathematical models or additional parameters to be specified, which can increase the likelihood of errors.
    3. The location and terrain of the data: The accuracy of the transformation can vary depending on the location and terrain of the data. Some areas may be more affected by tectonic movements or other changes in the Earth’s surface, which can make the transformation more challenging.
    4. The type of data being transformed: Different types of data (e.g. points, lines, polygons) may require different transformation methods or parameters, which can affect the accuracy of the transformation.

    While conversions between AGD66 and GDA94 can be relatively precise, they’re not 100% accurate.

    This is due to the inherent differences between the two datums, and the potential for errors or inaccuracies in the transformation process. It’s important to use appropriate transformation methods and understand the limitations and potential sources of error when converting data between different geodetic datums.

    The Difference Between GDA94 and GDA2020

    The key differences

    The main difference between GDA94 and GDA2020 is their accuracy and the methods used to define them.

    GDA2020 is a more accurate and up-to-date datum, with improvements in the modeling of the Earth’s surface that take into account changes in its shape over time. This means that positions and distances measured using GDA2020 are more accurate than those measured using GDA94. Additionally, GDA2020 is designed to be compatible with modern geospatial technologies and is expected to be used for many years to come.

    It’s worth noting that the difference between GDA94 and GDA2020 may not be significant for many applications, particularly those that don’t require high levels of accuracy. However, for applications that require precise positioning or measurement, such as surveying or mapping, selecting the correct geodetic datum is important to ensure accurate results.

    Differences in distance and direction

    The average distance and direction difference between GDA94 and GDA2020 depends on the location on the Earth’s surface.

    In general, the differences between the two datums are greatest in areas with high tectonic activity or areas where the Earth’s surface is undergoing significant changes, such as due to land subsidence or sea level rise.

    According to Geoscience Australia, the organisation responsible for geodetic information and services in Australia, the average difference between GDA94 and GDA2020 in Australia is around 1.5 meters. However, this value can vary significantly depending on the location, with some areas showing differences of several meters or more.

    The direction of the difference between the two datums also varies depending on the location, as it is related to the direction and magnitude of any tectonic movements or changes in the Earth’s surface. In general, the direction of the difference is determined by the vector between the two datums at a given location.

    It’s important to note that the difference between GDA94 and GDA2020 is not constant over time and may continue to change in the future. This is because the Earth’s surface is constantly changing due to tectonic activity, sea level rise, and other factors. As such, it’s important to regularly update geodetic data and use the most up-to-date geodetic datum for accurate positioning and navigation.

    Migrating from GDA94 to GDA2020

    The differences between the two means that migrating from GDA94 to GDA2020 can present several challenges and issues, particularly for organisations or projects that rely heavily on geospatial data.

    Some of the key issues with migrating to GDA2020 include: 

    1. Data compatibility: Data that was created using GDA94 may not be compatible with GDA2020. This can cause issues when trying to integrate or compare datasets that use different datums.
    2. Application compatibility: Applications that were designed to work with GDA94 may not be compatible with GDA2020. This can require updates or modifications to existing software or the adoption of new tools.
    3. Training and expertise: Staff who work with geospatial data may need to be trained on the new GDA2020 datum and its associated tools and workflows. This can take time and resources.
    4. Time and cost: Migrating to GDA2020 can be a complex and time-consuming process, particularly for large organisations or projects. There may be costs associated with updating software, purchasing new equipment, or retraining staff.
    5. Accuracy: While GDA2020 is a more accurate datum than GDA94, some existing data may still be more accurate when referenced to GDA94. This can make it difficult to compare or integrate data from different sources.
    6. Data transformation: In some cases, it may be necessary to transform data from GDA94 to GDA2020, which can introduce errors or inaccuracies. The accuracy of the transformation depends on the quality of the original data and the transformation method used.

    Migrating from GDA94 to GDA2020 requires careful planning and consideration of the potential issues and challenges. It’s crucial to work closely with geospatial experts and stakeholders to ensure a smooth and successful transition.

    What is WGS84 and Why is it Used by Software?

    WGS84 (World Geodetic System 1984) is a geodetic datum used for positioning and navigation purposes. It defines a reference system for the Earth’s surface that allows locations to be specified in latitude and longitude coordinates.

    The WGS84 datum was developed by the United States Department of Defense for use by the military and intelligence agencies, but it has since become the standard geodetic datum used by many organisations and applications around the world, including GPS (Global Positioning System) devices and mapping software.

    The WGS84 datum is based on a mathematical model of the Earth’s surface that takes into account its shape, size, and rotation. It defines a set of reference points and parameters that allow positions on the Earth’s surface to be accurately calculated and communicated.

    The WGS84 datum is widely used because it is compatible with GPS and other global navigation systems, allowing precise positioning and navigation in real-time. However, while there may be regional differences in the Earth’s surface that are not fully captured by the WGS84 model, that other geodetic datums may be more appropriate for certain applications or regions.

    How to Convert Between GDA2020 and WGS84

    To convert between GDA2020 (Geocentric Datum of Australia 2020) and WGS84 (World Geodetic System 1984), you can use coordinate transformation parameters provided by geodetic authorities. The transformation process involves converting coordinates from one datum to another using a mathematical model.

    In the case of GDA2020 and WGS84, the transformation parameters provided by the Intergovernmental Committee on Surveying and Mapping (ICSM) in Australia are known as the National Transformation Version 2 (NTv2) grid files. These grid files contain the necessary information for accurate transformations.

    The accuracy of the transformation depends on the specific region and the quality of the NTv2 grid files used. Always use the most up-to-date and accurate transformation parameters available from reputable sources.

    To convert coordinates between the GDA2020 (Geocentric Datum of Australia 2020) and WGS84 (World Geodetic System 1984) datums using Python, you can utilise the pyproj library. pyproj provides a convenient interface to the PROJ library, which is a widely used cartographic projection and coordinate transformation library.

    Geodetic Datum Usage in Australia

    In Australia, a lot of data providers offer data sets in both GDA94 and GDA2020 geodetic datums because the uptake of GDA2020 is not universal. It’s common practice for these providers to specify which datum was used to create each dataset.

    When combining geospatial datasets, it’s important for data professionals to ensure consistency in the geodetic datums employed.

    Using different datums without proper alignment can lead to inaccuracies, such as misaligning spatial features. For this reason, careful attention to datum consistency is essential to maintain the integrity and accuracy of integrated geospatial data.

     

    Originally published: 5 August, 2023

    Last updated: 11 February, 2025

     

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    Alternative Data: What Is It, Who Uses It And Why It Matters

    Alternative Data: What Is It, Who Uses It And Why It Matters

    Dec 12, 2024 | Data Domain

    Often in business, staying ahead means looking beyond the obvious.

    Alternative data, derived from non-traditional sources, offers a depth and uniqueness that traditional data sources can lack.

    In this blog, we’ll cover what alternative data is, how it differs from traditional data, who uses it, its role in business, and the pros and cons of integrating it into decision-making.

    Table of Contents

    Jump ahead ↓

    What Is Alternative Data?

    Alternative data (or Alt data) refers to non-traditional data sources that provide unique insights and information not typically captured by conventional data sets. The depth, detail, variety and uniqueness of alternative data are what make it such a powerful form of intel.

    Examples include:

    • Social media sentiment analysis
    • Geolocation data tracking foot traffic
    • Credit card transaction volumes
    • Satellite imagery
    • Drone imagery
    • Wearable tech data
    • Web traffic data
    • Mobile app usage statistics
    • IoT sensor data
    • Product reviews
    • Weather data
    • Flight data
    • App usage
    • ESG (environmental, social and corporate governance) data
    • Market prices
    • Company filings
    • Jet tracking

    Alternative data is far from a new concept. For centuries, astute business people have sought to understand their trading environments by observing the world through different lenses. These observations often generated insights—data that, while not traditional, provided a valuable edge in navigating markets effectively.

    What has changed in recent years is the remarkable accessibility of alternative data, driven by technological advancements across industries. This progress has expanded the volume and variety of data, making it easier for businesses to derive insights and make more informed decisions.

    Source: Casting the Net

    What is the Difference Between Alternative and Traditional Data?

    Traditional data comes from established, conventional sources like financial statements, government reports and structured databases. It is typically well-organised and standardised.

    Alternative data comes from unconventional, non-traditional sources. It is often unstructured or semi-structured, and thus less readily accessible or usable, and not easily searchable.

    It generally requires advanced processing techniques, substantial computational power and storage, as well as the ability to link seemingly unrelated pieces of information to develop a holistic understanding or derive meaningful insights.

    Who Uses Alternative Data?

    Alternative data is often used by hedge funds, private equity firms, investment banks and retail investors to gain insights and identify opportunities beyond traditional data sources.

    It is commonly used to:

    • Uncover potential risks not evident from traditional financial data.
    • Conduct stress tests and scenario analyses.
    • Identify and manage tail risks.
    • Discover new investment opportunities.
    • Reduce information asymmetry.
    • Optimise portfolio construction.

    Other specific examples include:

    • Environmental, Social and Governance (ESG)
      Alternative data like social media sentiment and news can be used to assess companies’ environmental, social, and governance (ESG) performance which can help investors incorporate ESG factors into decision-making.
    • Fintech
      Alternative data can also be used by fintech firms to offer tailored financial advice, identify potential customers and develop innovative investment products accessible to retail investors.

    Pros & Cons of Using Alternative Data

    Pros

    By using unconventional insights, businesses and investors can uncover opportunities and sharpen their strategies using alternative data. Here’s how it can help deliver an edge:

    More Detailed and Accurate Analysis

    Alt data provides additional data points of a business’s performance beyond traditional sources. When analysed holistically, it can potentially offer a more complete understanding of business performance and customer loyalty.

    Decisions Backed by Historical Trends

    The access to historical data enables predictive analysis, helping anticipate outcomes, identify opportunities and avoid potential risks.

    Improved Investments and Partnerships

    A broader range of data points allows businesses to assess compatibility with potential partners and make more strategic, mutually beneficial investment decisions.

    Enhanced Customer Relationships

    Data on customer feedback, web traffic and audience demographics helps businesses refine strategies to improve customer satisfaction and loyalty.

    Competitive Advantage

    Real-time insights allow quicker, informed decisions, helping businesses stay ahead of competitors reliant on traditional data.

    Cons

    While alternative data offers exciting possibilities, it’s not without its hurdles. Effectively leveraging these unconventional insights requires addressing some critical challenges. Here are the key drawbacks businesses might consider:

    Inconsistent Data Quality

    Due to the wide variety and the varied applications, alt data is hard to regulate. It can come aggregated or as a straight data feed. The lack of standardisation and rules around alt data means these types of data sets may contain errors, which can lead to misinterpretations which, in turn, affects decision-making.

    Transparency and Ethical Concerns

    Data collection methods, such as tracking GPS or online activity, may erode consumer trust if it is seen as invasive or unethical.

    Privacy and Security Risks

    Sensitive data usage exposes businesses to breaches, legal violations and potential harm to individuals. This can pose serious risks to a brand’s reputation and business compliance.

    Discrimination and Bias Risks

    Using personal demographics in decision-making can lead to unintentional discrimination, flawed data sets and long-term reputational damage.

    Manipulated Data Variables

    Publicly available data can sometimes be intentionally skewed to serve specific interests—businesses might highlight only positive reviews, while individuals may curate information to boost their credibility. This highlights the importance of adopting a comprehensive approach, drawing from a diverse range of data channels rather than relying on a single source.

    The True Value of Alternative Data

    Alternative or alt data offers deeper insights, empowering businesses and investors to make more informed decisions and gain a competitive edge.

    As accessibility to alt data continues to grow, so do the opportunities to harness its potential in creative and transformative ways.

    But challenges like data quality, transparency and privacy means that alt data requires careful management.

    By adopting a thoughtful and ethical approach, progressive businesses can tap into alt data’s inherent value to inspire smarter investments, spark innovations and deliver groundbreaking solutions.

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    What Is A Geohash And How Is It Used?

    What Is A Geohash And How Is It Used?

    Nov 25, 2024 | Data Domain

    What is a geohash and how is it used? We break it down here.

    Table of Contents

    Jump ahead ↓

    What Is A Geohash?

    Geohashing is a geocoding system created in 2008 that encodes geographic coordinates (latitude and longitude) into a short and compact string of characters made up of letters and numbers. The resulting compact string of characters is a Geohash.

    Think of it like a secret code for locations on Earth – a short and easy-to-understand code. It turns a place’s latitude and longitude, which are like its coordinates on a big map, into a simple code.

    For example, the coordinates “latitude 40.7128 degrees North and longitude 74.0060 degrees West” is the same as geohash “6gkzwgjz.” – this shorter code pinpoints the same location but is much easier to share or remember.

    In a nutshell, a geohash is a clever way to turn complex latitude and longitude coordinates into simple codes that make it easier to talk about places and share location information.

    This is also what makes geohash a versatile tool, making it easier to work with location-based data and services in a wide range of industries and applications.

    Breaking Down Geohash – How the World is Encoded into Precise Rectangles

    The main concept behind Geohash is to divide the world into a grid of rectangles and then represent each of these rectangles with a unique code. This code becomes shorter as you zoom out to cover larger areas and gets longer as you zoom in for more precision.

    Here’s a technical explanation of how Geohash works:

    1. Dividing the world into rectangles
      Geohash starts by dividing the Earth into a grid of rectangles. The whole Earth is initially represented by one big rectangle.
    2. Choosing a binary representation
      Each rectangle is further divided into smaller rectangles. Geohash uses a binary search approach to determine which half of a rectangle contains a given point.
      It assigns a binary digit of 1 for the upper half of the rectangle and 0 for the lower half.
    3. Building the geohash string
      Starting with the whole Earth as one big rectangle, geohash determines whether a point falls into the top or bottom half (binary digit 1 or 0) and appends that digit to the geohash string.The Earth is then divided into the top or bottom half based on the chosen digit.
      This process is repeated iteratively until you have the desired level of precision or length in the geohash string.
    4. Precision and length of the geohash
      The length of the geohash string determines the level of precision: A shorter geohash represents a larger area, while a longer geohash represents a smaller and more precise area:
      – A Geohash of length 1 represents a large area, such as a continent.

      – A Geohash of length 2 represents a smaller area, such as a country.
      – A Geohash of length 3 represents a region or large city.
      – A Geohash of length 4 represents a smaller city or town.
      – Geohashes of length 5 or higher represent progressively smaller areas, down to street level and even specific locations.

    5. Encoding characters
      To enable the geohash string to be more easily read by humans and generally be more user-friendly, it translates the binary digits into a set of characters. Geohash typically uses a character set of 32 characters: 0-9 and the letters a through z (excluding i, l, o).
    6. Final geohash
      The result is a geohash string that represents a specific geographic location. This string is both compact and can be easily shared or stored.

    What Does Geohash Look Like?

    Geohash divides the world into a grid of rectangles, as indicated in this portion of the world map.

    Geohash

    Source: Geohash Explorer

    Use Cases of Geohash

    Geohash is widely used for its simplicity and efficiency in spatial indexing, making it useful for a wide range of practical applications across industries.

    • Location-based searches
      Geohash is used in location-based search engines and applications, showing nearby points of interest, businesses, or places based on their current location or a specified area.
    • Geofencing
    • Geohash is employed to create geofences, or virtual boundaries, which can be used for triggering actions or alerts when a device or user enters or exits a specific geographic area, such as location-based marketing, asset tracking, security applications, social networking and even dating apps.
    • Mapping and cartography
      Geohash is used in cartography to represent geographic data efficiently. It can simplify the storage and retrieval of spatial information in mapping systems.
    • Digital adventures
      Users can connect with people and discover new destinations worldwide via apps which leverage geohashes to enhance travel journaling and social interactions, making global adventures more interactive and engaging.

    These are just a few examples, and the applications of geohash continue to expand as location-based data becomes increasingly important across various domains.

    The simplified representation of geographic coordinates makes geohash more accessible and user-friendly, which is why it is such a versatile geocoding system with a wide range of uses.

    How Does Geohash Compare With Other Spatial Encoding Systems?

    A geohash is one of many spatial encoding systems designed to represent geographic locations compactly and efficiently. Geohash can be substituted with alternative systems depending on the specific requirements of the application.

    Each spatial encoding system is tailored to address particular needs, offering unique features and trade-offs that make some systems more suitable for specific use cases. For example, geohashes excel in simplicity and spatial indexing, while systems like H3 or Plus Codes may provide advantages in precision, scalability, or user-friendliness.

    Read our blog which compares Geohash with H3>

    Originally published: 26 September, 2023

    Last updated: 26 November, 2024

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